This document discusses developing GPS-free positioning for utility vehicles in specialty agriculture using wheel encoders, laser range scanners, and an extended Kalman filter localization algorithm. It aims to provide sub-meter positioning accuracy without relying on GPS due to signal occlusion from trees and structures. The experimental platform uses sensors to measure relative motion and range/bearing to known landmarks stored in a pre-built map. The localization filter estimates the vehicle's pose by predicting its position from previous readings and correcting it based on sensor measurements of landmarks. Initial tests achieved positioning errors close to the sub-meter accuracy goal.
Dotnet t-drive enhancing driving directions with taxi drivers’ intelligenceEcway Technologies
This paper presents a smart driving direction system that utilizes taxi trajectory data to model dynamic traffic patterns and the routing intelligence of experienced taxi drivers. The system represents this information as a time-dependent landmark graph and uses a clustering approach to estimate travel times between landmarks in different time slots. It then designs a two-stage routing algorithm to compute the fastest and most customized route for users based on their departure time. Evaluation on a real-world dataset of over 33,000 taxis over three months found that the system's routes were faster than competitors 60-70% of the time and equally fast 20% of the time, with an average speed improvement of 50% or more.
Sai teja madireddy gave a technical seminar on Google's driverless car at Audisanakra Institute of Technology. The presentation covered the history and development of Google's driverless car project, its key components including Google Maps, hardware sensors like LIDAR and video cameras, and artificial intelligence software. It was noted that Google's driverless cars have driven over 140,000 miles with only occasional human intervention required. The development of driverless car technology was concluded to help improve vehicle stability and safety by reducing human error in driving.
The document discusses Google's self-driving car. It has sensors like LIDAR and cameras that generate 3D maps of the environment. The car uses these maps along with GPS and AI to navigate roads autonomously, obeying traffic laws. Some benefits are reduced accidents and increased road capacity. Challenges include hackers potentially interfering with the system or failures causing accidents. The car aims to safely transport passengers to their destinations using sensors and software.
Google driverless car technical seminar report (.docx)gautham p
Google Driverless Car is the latest technology or innovation that is going to hit the market in the coming years.
This report is especially for mechanical engineering students.
Google has developed technology for autonomous vehicles called Google Chauffeur. The project is led by Sebastian Thrun, who previously won the DARPA Grand Challenge and co-invented Google Street View. The cars use Google Maps to provide road information, hardware sensors to detect the environment in real time, and artificial intelligence to make decisions about speed, steering, and braking. The cars have driven nearly 700,000 autonomous miles in road testing, with a new prototype revealed in 2014 that has no steering wheel or pedals and is fully autonomous.
The document discusses the features and technologies of hybrid cars. Hybrid cars can use vision systems to detect other vehicles, traffic signals, pedestrians and obstacles. They can also monitor the driver's physiology, road and weather conditions. Key features include collision avoidance systems, adaptive cruise control, imaging technologies and navigation aids to improve safety. The hybrid cars of the future are expected to have more advanced autonomous capabilities and communication between vehicles and infrastructure to further reduce accidents.
This document discusses automated or driverless cars. It describes how driverless cars use sensors like LIDAR and radar along with artificial intelligence, GPS, and Google Maps to navigate without human intervention. The car's AI software is connected to all sensors and controls systems like steering and brakes based on input from sensors and maps. Major companies developing driverless car technology include Google, GM, Ford, Audi, BMW, Volkswagen and Volvo. Benefits include eliminating accidents from human error, improving traffic flow, and allowing passengers to work or rest while the car drives itself.
Dotnet t-drive enhancing driving directions with taxi drivers’ intelligenceEcway Technologies
This paper presents a smart driving direction system that utilizes taxi trajectory data to model dynamic traffic patterns and the routing intelligence of experienced taxi drivers. The system represents this information as a time-dependent landmark graph and uses a clustering approach to estimate travel times between landmarks in different time slots. It then designs a two-stage routing algorithm to compute the fastest and most customized route for users based on their departure time. Evaluation on a real-world dataset of over 33,000 taxis over three months found that the system's routes were faster than competitors 60-70% of the time and equally fast 20% of the time, with an average speed improvement of 50% or more.
Sai teja madireddy gave a technical seminar on Google's driverless car at Audisanakra Institute of Technology. The presentation covered the history and development of Google's driverless car project, its key components including Google Maps, hardware sensors like LIDAR and video cameras, and artificial intelligence software. It was noted that Google's driverless cars have driven over 140,000 miles with only occasional human intervention required. The development of driverless car technology was concluded to help improve vehicle stability and safety by reducing human error in driving.
The document discusses Google's self-driving car. It has sensors like LIDAR and cameras that generate 3D maps of the environment. The car uses these maps along with GPS and AI to navigate roads autonomously, obeying traffic laws. Some benefits are reduced accidents and increased road capacity. Challenges include hackers potentially interfering with the system or failures causing accidents. The car aims to safely transport passengers to their destinations using sensors and software.
Google driverless car technical seminar report (.docx)gautham p
Google Driverless Car is the latest technology or innovation that is going to hit the market in the coming years.
This report is especially for mechanical engineering students.
Google has developed technology for autonomous vehicles called Google Chauffeur. The project is led by Sebastian Thrun, who previously won the DARPA Grand Challenge and co-invented Google Street View. The cars use Google Maps to provide road information, hardware sensors to detect the environment in real time, and artificial intelligence to make decisions about speed, steering, and braking. The cars have driven nearly 700,000 autonomous miles in road testing, with a new prototype revealed in 2014 that has no steering wheel or pedals and is fully autonomous.
The document discusses the features and technologies of hybrid cars. Hybrid cars can use vision systems to detect other vehicles, traffic signals, pedestrians and obstacles. They can also monitor the driver's physiology, road and weather conditions. Key features include collision avoidance systems, adaptive cruise control, imaging technologies and navigation aids to improve safety. The hybrid cars of the future are expected to have more advanced autonomous capabilities and communication between vehicles and infrastructure to further reduce accidents.
This document discusses automated or driverless cars. It describes how driverless cars use sensors like LIDAR and radar along with artificial intelligence, GPS, and Google Maps to navigate without human intervention. The car's AI software is connected to all sensors and controls systems like steering and brakes based on input from sensors and maps. Major companies developing driverless car technology include Google, GM, Ford, Audi, BMW, Volkswagen and Volvo. Benefits include eliminating accidents from human error, improving traffic flow, and allowing passengers to work or rest while the car drives itself.
The document discusses autonomous or driverless cars. It provides details about how an autonomous car can navigate to a destination on its own using sensors like radar, lidar, GPS and computer vision to detect its environment without human input. It describes some of the key technologies used in autonomous cars like laser rangefinders, cameras and sensors that allow the vehicles to drive themselves while avoiding obstacles and obeying traffic laws. The document also discusses some of the challenges in developing autonomous vehicles and getting the technology to safely operate without human drivers.
The document discusses Google's driverless car project. It describes how the car can steer, accelerate, and stop itself using sensors like LIDAR and cameras to detect obstacles and traffic conditions. The car's artificial intelligence analyzes data from Google Maps and sensors to determine how to drive safely. As of 2012, Google had 6 driverless cars that had traveled over 140,000 miles on public roads in Nevada with only occasional human intervention needed. Benefits include reduced accidents, easier traffic management, and increased road capacity. Potential risks include hacking and sensor failures.
Waymo is an autonomous vehicle company that was started as Google's self-driving car project in 2009. It uses hardware sensors like LIDAR, radar, cameras and software to allow vehicles to drive themselves. The goal is to prevent traffic accidents, reduce emissions and free up people's time. Sebastian Thrun led the early research and development. Nevada was the first state to pass a law allowing driverless cars in 2011. Google has tested over 140,000 miles with its fleet of Toyota Prius and Audi TT vehicles. Advantages include safety and efficiency while disadvantages include potential hacking risks. Future applications could include shared autonomous taxis and increased road capacity.
FME Before You Dig: The Sunesys One Call Automated Response SystemSafe Software
This document describes Sunesys' use of FME for managing their OneCall ticketing system. Key points:
- FME is used to automatically update Sunesys' registered underground facility locations with each state's OneCall system monthly.
- A process called SOCARS uses FME to spatially analyze tickets, identify conflicts, send emails to contractors, and post response codes - reducing costs compared to their old manual process.
- SOCARS allows accurate tracking of tickets and invoices through its automated workflow and database timestamps.
Waymo was originally a Google self-driving car project and is now a standalone company called Waymo. Waymo's mission is to make transportation safe, easy, and accessible to all without requiring a human driver. Waymo's self-driving cars use sensors like radar, lidar, and cameras to detect surroundings from long distances in all directions. The information from these sensors is analyzed by a central computer that controls the vehicle's steering, acceleration, and braking. Waymo has been testing self-driving cars on public roads since 2009 and launched a pilot program in Phoenix, AZ in 2017 for residents to ride in the self-driving vehicles.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
GPS navigation systems use trilateration to determine a user's location based on distance measurements to multiple satellites. A GPS receiver locates at least 3 satellites, calculates the distance to each using signal travel time, and deduces the intersection point as its location. However, GPS faces challenges from atmospheric delays and signal reflections that can impact accuracy. Differential GPS helps correct errors by gauging inaccuracy at fixed stations to broadcast corrections to nearby receivers.
This document describes the design and implementation of a GPS-based device for navigation. It begins with an introduction to GPS basics like how GPS works via trilateration of signals from multiple satellites. It then details the hardware components of the device including the GPS module, microcontroller, and display. The document explains how the device determines location by receiving GPS signals and processing them with the microcontroller. It also discusses ways to improve accuracy through differential GPS and lists several real-world applications like vehicle tracking, navigation, and timing where GPS is currently used. In conclusion, it envisions potential future upgrades and broader uses of the technology.
Precision agriculture uses GPS and other technologies like remote sensing, GIS, and yield monitors to optimize crop management based on spatial and temporal variability within fields. The document discusses two case studies on precision agriculture in India. The first found that adopting precision farming techniques like variable fertilizer application and drip irrigation increased tomato and brinjal yields and profits for small-scale farmers. The second presented a method for using GPS to automatically map the locations of transplanted crops in real-time, reducing costs compared to other precision agriculture mapping systems.
GPS technology enables precision agriculture by allowing farmers to precisely locate their position in the field, monitor soil characteristics on a detailed grid, and automate agricultural machinery. GPS uses a constellation of satellites 12,000 miles above the Earth to pinpoint locations 24 hours a day anywhere globally. Farmers can now collect real-time data on their fields, target fertilizer and pesticide application only where needed, and automate tractors for efficient field work. This precision allows for reduced costs, less environmental pollution, and improved farm management decisions.
Application of remote sensing in agriculturevajinder kalra
The document discusses the concepts and applications of remote sensing, GIS, and GPS in agriculture. It defines remote sensing as sensing things from a distance using electromagnetic radiation and describes the different platforms (ground, air, satellite) used. It explains key remote sensing concepts including spectral signatures, spectral reflectance curves, spatial/spectral/radiometric/temporal resolutions, and indices like NDVI. Interpretation of remote sensing imagery involves analyzing tone, shape, size, pattern, texture, shadow, and association. Spectral signatures can provide information about vegetation, soil moisture, organic matter, iron content, and other properties. Remote sensing allows monitoring and analyzing agriculture from a distance.
The document discusses the benefits of exercise for both physical and mental health. It notes that regular exercise can reduce the risk of diseases like heart disease and diabetes, improve mood, and reduce feelings of stress and anxiety. The document recommends that adults get at least 150 minutes of moderate exercise or 75 minutes of vigorous exercise per week to gain these benefits.
This document discusses driverless cars, including their components, functions, working, advantages and disadvantages. Driverless cars use sensors like radar, lidar, computer vision and GPS to detect their surroundings and navigate roads autonomously. They have control systems that analyze sensor data to identify other vehicles. Key components are sensors, a control unit and actuators that allow the computer to safely operate the vehicle. While driverless cars could solve traffic issues and reduce labor costs, challenges include relying on accurate high-quality maps and requiring sophisticated AI and sensing technologies.
Rohan Divekar has a Master's degree in Electrical Engineering with a focus on control systems. He has over 5 years of experience developing algorithms for advanced driver assistance systems and autonomous vehicle technologies like adaptive cruise control, cooperative intersection collision avoidance using vehicle-to-vehicle communication, and cooperative adaptive cruise control. His skills include system identification, Kalman filtering, mathematical modeling, and hardware-in-loop simulation. He is currently a Controls Engineer at Magna Electronics.
This document provides an overview of mobile mapping systems for surveying. It discusses how mobile mapping uses sensors mounted on vehicles to digitally map areas. The main sensors discussed are GNSS receivers, IMUs, distance measurement instruments, LiDAR, and cameras. These can be mounted on vehicle, handheld, or trolley-based platforms. Applications mentioned include road assessment, building information modeling, emergency response, vegetation mapping, and digital heritage conservation.
This document presents research on automatic camera calibration for vision-based traffic speed sensing. Traditional sensors have limitations, so the researchers developed a system using machine vision with a camera. It uses pattern detection-based vehicle tracking to initialize calibration and estimate vanishing points. This allows automatic calibration of pan-tilt-zoom cameras without manual setup. The algorithm was tested and achieved real-time vehicle detection, tracking, classification and calibration. Future work aims to improve accuracy by incorporating lane markings and develop online tuning of the system.
This document presents research on automatic camera calibration for vision-based traffic speed sensing. Traditional sensors have limitations, so the researchers developed a system using machine vision with a pattern detection-based tracking algorithm. It automatically calibrates pan-tilt-zoom cameras without manual setup by estimating vanishing points and performing RANSAC fitting on vehicle tracking data. This allows real-time detection, tracking and classification of vehicles under varying conditions without road markings or predefined parameters. A demonstration of the system showed it can eliminate false alarms while handling occlusion. Future work will refine the calibration and enable turn counting at intersections.
Towards Rapid Implementation of Adaptive Robotic SystemsMeshDynamics
Current automation design practice produces expensive one-of-a-kind installations where the system cannot be easily modified to
meet changing demands or advancements in technology. It is imperative that we design robot systems to be modular, portable and
easily re-configurable in order to reduce the design lead times and life cycle costs of providing automation alternatives.
The Unified Tele-robotics Architecture Program (UTAP) was developed under the sponsorship of the US Air Force Robotics and
Automation Center of Excellence. A goal of the program was to define and develop prototypes of commonly used software building
blocks for sensor guided real time embedded control of telerobotic devices. Standard building blocks and a non-proprietary
communication protocols would provide the Air Force and specifically the Logistic Centers with a support infrastructure designed to
rapidly and efficiently build and maintain mission critical automation systems.
Shane McDermott of Mid-West GIS presented on using GPS for field inventories. He discussed the different types of GPS technologies including real-time kinematic GPS which provides sub-centimeter accuracy in real time. Mid-West GIS uses RTK GPS with data collectors and a geodatabase model to accurately map and inventory utility assets for clients. The highly accurate location data allows clients to precisely know the location and attributes of assets.
This document discusses using geospatial imagery for location intelligence. It describes different types of imagery like satellite, drone, and street-level images. Intelligence can be derived from imagery by extracting features, detecting changes, and regular monitoring. Examples are given of using deep learning for feature extraction from satellite imagery and generating building footprints. Facebook and Microsoft have used AI to generate road maps from imagery which are then reviewed and added to OpenStreetMap. Street-level imagery from Mapillary also helps map features.
Cloud Based Autonomous Vehicle NavigationWilliam Smith
This document proposes a cloud-based autonomous vehicle control and navigation system that allows vehicles to cooperatively sense obstacles and avoid traffic. It involves the following:
1. RC cars equipped with sensors and microprocessors that can detect obstacles and communicate with a remote server via cloud.
2. A remote server runs an algorithm to determine the optimal path for each vehicle based on its location and destination, using real-time sensor data from vehicles about obstacles.
3. The system aims to scale to large numbers of autonomous vehicles by distributing sensing and control - vehicles only need to sense their environment and follow path instructions from the remote server.
The document discusses autonomous or driverless cars. It provides details about how an autonomous car can navigate to a destination on its own using sensors like radar, lidar, GPS and computer vision to detect its environment without human input. It describes some of the key technologies used in autonomous cars like laser rangefinders, cameras and sensors that allow the vehicles to drive themselves while avoiding obstacles and obeying traffic laws. The document also discusses some of the challenges in developing autonomous vehicles and getting the technology to safely operate without human drivers.
The document discusses Google's driverless car project. It describes how the car can steer, accelerate, and stop itself using sensors like LIDAR and cameras to detect obstacles and traffic conditions. The car's artificial intelligence analyzes data from Google Maps and sensors to determine how to drive safely. As of 2012, Google had 6 driverless cars that had traveled over 140,000 miles on public roads in Nevada with only occasional human intervention needed. Benefits include reduced accidents, easier traffic management, and increased road capacity. Potential risks include hacking and sensor failures.
Waymo is an autonomous vehicle company that was started as Google's self-driving car project in 2009. It uses hardware sensors like LIDAR, radar, cameras and software to allow vehicles to drive themselves. The goal is to prevent traffic accidents, reduce emissions and free up people's time. Sebastian Thrun led the early research and development. Nevada was the first state to pass a law allowing driverless cars in 2011. Google has tested over 140,000 miles with its fleet of Toyota Prius and Audi TT vehicles. Advantages include safety and efficiency while disadvantages include potential hacking risks. Future applications could include shared autonomous taxis and increased road capacity.
FME Before You Dig: The Sunesys One Call Automated Response SystemSafe Software
This document describes Sunesys' use of FME for managing their OneCall ticketing system. Key points:
- FME is used to automatically update Sunesys' registered underground facility locations with each state's OneCall system monthly.
- A process called SOCARS uses FME to spatially analyze tickets, identify conflicts, send emails to contractors, and post response codes - reducing costs compared to their old manual process.
- SOCARS allows accurate tracking of tickets and invoices through its automated workflow and database timestamps.
Waymo was originally a Google self-driving car project and is now a standalone company called Waymo. Waymo's mission is to make transportation safe, easy, and accessible to all without requiring a human driver. Waymo's self-driving cars use sensors like radar, lidar, and cameras to detect surroundings from long distances in all directions. The information from these sensors is analyzed by a central computer that controls the vehicle's steering, acceleration, and braking. Waymo has been testing self-driving cars on public roads since 2009 and launched a pilot program in Phoenix, AZ in 2017 for residents to ride in the self-driving vehicles.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
GPS navigation systems use trilateration to determine a user's location based on distance measurements to multiple satellites. A GPS receiver locates at least 3 satellites, calculates the distance to each using signal travel time, and deduces the intersection point as its location. However, GPS faces challenges from atmospheric delays and signal reflections that can impact accuracy. Differential GPS helps correct errors by gauging inaccuracy at fixed stations to broadcast corrections to nearby receivers.
This document describes the design and implementation of a GPS-based device for navigation. It begins with an introduction to GPS basics like how GPS works via trilateration of signals from multiple satellites. It then details the hardware components of the device including the GPS module, microcontroller, and display. The document explains how the device determines location by receiving GPS signals and processing them with the microcontroller. It also discusses ways to improve accuracy through differential GPS and lists several real-world applications like vehicle tracking, navigation, and timing where GPS is currently used. In conclusion, it envisions potential future upgrades and broader uses of the technology.
Precision agriculture uses GPS and other technologies like remote sensing, GIS, and yield monitors to optimize crop management based on spatial and temporal variability within fields. The document discusses two case studies on precision agriculture in India. The first found that adopting precision farming techniques like variable fertilizer application and drip irrigation increased tomato and brinjal yields and profits for small-scale farmers. The second presented a method for using GPS to automatically map the locations of transplanted crops in real-time, reducing costs compared to other precision agriculture mapping systems.
GPS technology enables precision agriculture by allowing farmers to precisely locate their position in the field, monitor soil characteristics on a detailed grid, and automate agricultural machinery. GPS uses a constellation of satellites 12,000 miles above the Earth to pinpoint locations 24 hours a day anywhere globally. Farmers can now collect real-time data on their fields, target fertilizer and pesticide application only where needed, and automate tractors for efficient field work. This precision allows for reduced costs, less environmental pollution, and improved farm management decisions.
Application of remote sensing in agriculturevajinder kalra
The document discusses the concepts and applications of remote sensing, GIS, and GPS in agriculture. It defines remote sensing as sensing things from a distance using electromagnetic radiation and describes the different platforms (ground, air, satellite) used. It explains key remote sensing concepts including spectral signatures, spectral reflectance curves, spatial/spectral/radiometric/temporal resolutions, and indices like NDVI. Interpretation of remote sensing imagery involves analyzing tone, shape, size, pattern, texture, shadow, and association. Spectral signatures can provide information about vegetation, soil moisture, organic matter, iron content, and other properties. Remote sensing allows monitoring and analyzing agriculture from a distance.
The document discusses the benefits of exercise for both physical and mental health. It notes that regular exercise can reduce the risk of diseases like heart disease and diabetes, improve mood, and reduce feelings of stress and anxiety. The document recommends that adults get at least 150 minutes of moderate exercise or 75 minutes of vigorous exercise per week to gain these benefits.
This document discusses driverless cars, including their components, functions, working, advantages and disadvantages. Driverless cars use sensors like radar, lidar, computer vision and GPS to detect their surroundings and navigate roads autonomously. They have control systems that analyze sensor data to identify other vehicles. Key components are sensors, a control unit and actuators that allow the computer to safely operate the vehicle. While driverless cars could solve traffic issues and reduce labor costs, challenges include relying on accurate high-quality maps and requiring sophisticated AI and sensing technologies.
Rohan Divekar has a Master's degree in Electrical Engineering with a focus on control systems. He has over 5 years of experience developing algorithms for advanced driver assistance systems and autonomous vehicle technologies like adaptive cruise control, cooperative intersection collision avoidance using vehicle-to-vehicle communication, and cooperative adaptive cruise control. His skills include system identification, Kalman filtering, mathematical modeling, and hardware-in-loop simulation. He is currently a Controls Engineer at Magna Electronics.
This document provides an overview of mobile mapping systems for surveying. It discusses how mobile mapping uses sensors mounted on vehicles to digitally map areas. The main sensors discussed are GNSS receivers, IMUs, distance measurement instruments, LiDAR, and cameras. These can be mounted on vehicle, handheld, or trolley-based platforms. Applications mentioned include road assessment, building information modeling, emergency response, vegetation mapping, and digital heritage conservation.
This document presents research on automatic camera calibration for vision-based traffic speed sensing. Traditional sensors have limitations, so the researchers developed a system using machine vision with a camera. It uses pattern detection-based vehicle tracking to initialize calibration and estimate vanishing points. This allows automatic calibration of pan-tilt-zoom cameras without manual setup. The algorithm was tested and achieved real-time vehicle detection, tracking, classification and calibration. Future work aims to improve accuracy by incorporating lane markings and develop online tuning of the system.
This document presents research on automatic camera calibration for vision-based traffic speed sensing. Traditional sensors have limitations, so the researchers developed a system using machine vision with a pattern detection-based tracking algorithm. It automatically calibrates pan-tilt-zoom cameras without manual setup by estimating vanishing points and performing RANSAC fitting on vehicle tracking data. This allows real-time detection, tracking and classification of vehicles under varying conditions without road markings or predefined parameters. A demonstration of the system showed it can eliminate false alarms while handling occlusion. Future work will refine the calibration and enable turn counting at intersections.
Towards Rapid Implementation of Adaptive Robotic SystemsMeshDynamics
Current automation design practice produces expensive one-of-a-kind installations where the system cannot be easily modified to
meet changing demands or advancements in technology. It is imperative that we design robot systems to be modular, portable and
easily re-configurable in order to reduce the design lead times and life cycle costs of providing automation alternatives.
The Unified Tele-robotics Architecture Program (UTAP) was developed under the sponsorship of the US Air Force Robotics and
Automation Center of Excellence. A goal of the program was to define and develop prototypes of commonly used software building
blocks for sensor guided real time embedded control of telerobotic devices. Standard building blocks and a non-proprietary
communication protocols would provide the Air Force and specifically the Logistic Centers with a support infrastructure designed to
rapidly and efficiently build and maintain mission critical automation systems.
Shane McDermott of Mid-West GIS presented on using GPS for field inventories. He discussed the different types of GPS technologies including real-time kinematic GPS which provides sub-centimeter accuracy in real time. Mid-West GIS uses RTK GPS with data collectors and a geodatabase model to accurately map and inventory utility assets for clients. The highly accurate location data allows clients to precisely know the location and attributes of assets.
This document discusses using geospatial imagery for location intelligence. It describes different types of imagery like satellite, drone, and street-level images. Intelligence can be derived from imagery by extracting features, detecting changes, and regular monitoring. Examples are given of using deep learning for feature extraction from satellite imagery and generating building footprints. Facebook and Microsoft have used AI to generate road maps from imagery which are then reviewed and added to OpenStreetMap. Street-level imagery from Mapillary also helps map features.
Cloud Based Autonomous Vehicle NavigationWilliam Smith
This document proposes a cloud-based autonomous vehicle control and navigation system that allows vehicles to cooperatively sense obstacles and avoid traffic. It involves the following:
1. RC cars equipped with sensors and microprocessors that can detect obstacles and communicate with a remote server via cloud.
2. A remote server runs an algorithm to determine the optimal path for each vehicle based on its location and destination, using real-time sensor data from vehicles about obstacles.
3. The system aims to scale to large numbers of autonomous vehicles by distributing sensing and control - vehicles only need to sense their environment and follow path instructions from the remote server.
Research presentation on Autonomous Driving. Direction perception approach.
Research work by Princeton University group.
Note: Link given in the presentation
Summer research project that include evaluate two online camera calibration algorithms and use the algorithm with better test result to perform back-projection and geo-location for pedestrian to be virtualized in 3D model.
An autopilot is a system used to control the trajectory of an aircraft, marine craft or spacecraft without requiring constant manual control by a human operator. Autopilots do not replace human operators. Instead, the autopilot assists the operator's control of the vehicle, allowing the operator to focus on broader aspects of operations (for example, monitoring the trajectory, weather and on-board systems)
The document summarizes the architectures of three self-driving vehicles that competed in the 2007 DARPA Urban Challenge: Talos (MIT), Boss (CMU), and Junior (Stanford). All three vehicles used similar sensing technologies like LiDAR and radar for perception tasks like obstacle detection and tracking. They also had components for localization, mapping, planning paths and behaviors. Talos stood out for its unified planning and control system, Boss for its behavioral executive, and Junior for its precise localization. The Challenge marked early progress in autonomous driving and showcased different technical approaches to navigation in urban environments.
Role of localization and environment perception in autonomous drivingQualcomm Research
Dheeraj Ahuja, Sr. Director at Qualcomm Technologies, discusses how localization and perception technologies are critical for enhanced autonomous driving. As autonomous levels increase from active safety to full self-driving, requirements become more complex. Key technologies discussed include radar, camera, lidar, HD maps, and Qualcomm's VEPP precise positioning. Qualcomm's approach focuses on sensor fusion from cameras, radars, lidars and 5G to provide robust perception for autonomous vehicles.
The document discusses the design of a robotic system called SPARSH. It involves implementing techniques like machine vision, simultaneous localization and mapping (SLAM), and path detection algorithms using cameras to allow the robot to navigate autonomously and map its surroundings. The robot will be tested to perform surveillance and disaster management tasks. The project aims to develop an advanced, field-ready robotic system incorporating solar charging by 2014 through continued work by student teams.
The document discusses safety precautions for autonomous vehicles including forward collision warning systems (FCW) and adaptive cruise control (ACC) systems. FCW uses radar sensors to detect objects ahead and warn the driver, while ACC uses radar, GPS, and map data to automatically control vehicle speed and braking to maintain a safe distance from the vehicle ahead. Key advantages are convenience and more relaxed driving, while disadvantages include potential issues with sensor visibility and reliance on external systems.
The document discusses autonomous or self-driving cars. It describes how autonomous cars use sensors like LIDAR, radar, cameras and ultrasonic sensors along with GPS and an inertial measurement unit to navigate without human intervention. The central computer combines data from these sensors to construct a 3D map of the vehicle's surroundings and control systems like steering and braking. Major companies developing autonomous vehicle technology include Google, Audi, BMW, Ford and General Motors.
Similar to Accurate GPS-free Positioning of Utility Vehicles for Specialty Agriculture (20)
This document describes an on-the-go device that uses lasers and cameras to measure tree calipers and count trees in nurseries. It can measure calipers to within +/- 2.5 mm for unstaked trees and provide counts with over 95% accuracy. The device uses two lasers and a 60 Hz camera to capture multiple frames of each tree from different angles as it travels down rows at speeds up to 3 mph. It has been tested in several nurseries on different tree types with good accuracy. Localization software can also track the device's path within a nursery to within sub-meter accuracy.
The document summarizes research on developing an automated system called the Z-Trap to monitor insect populations as an alternative to current manual methods. The Z-Trap uses sensors and electronics to detect and count insects caught in the trap and wirelessly transmits the data, providing a more efficient way to monitor insect populations and help determine pesticide application needs. Field tests of prototype Z-Traps were conducted in 2011 at orchards in Washington and Pennsylvania to evaluate their effectiveness.
This document proposes a four station vacuum and filling apple harvester unit with a work platform and trailer. The harvester uses vacuum to collect apples and has four filling stations to load the apples into bins or boxes. It includes a work platform and will be trailer-mounted for easy transportation between orchards.
Gwen Hoheisel narrates a presentation about the Washington Tree Fruit Research Commission's November 2010 trip to Italy's Trade Shows and Orchard Tours
The document discusses the market opportunity for specialty crop automation for mid-sized farms between 25-1000 acres. It notes that these mid-sized farms represent a larger opportunity than smaller farms due to scale, and larger farms due to already having custom automation. The hypothesis is that automating specialty crops for mid-sized farms could address an unmet need and solve business problems in a market with sufficient scale. Next steps proposed include testing this hypothesis, observing customers to identify opportunities, understanding the business model better, and developing prototypes.
Researchers developed new electronic insect traps to automatically monitor moths in orchards. Prototype traps from 2009 had poor capture rates compared to standard traps, possibly due to repellent effects. A new "Z-trap" using bio-impedance performed better in 2010 trials against codling moth and oriental fruit moth than standard traps. Infrared traps also showed potential. Researchers continued improving and testing designs, analyzing signal data, and aiming to incorporate features into a single integrated trap design that could automatically report detections to users. The work was funded by grants and supported by collaborators from multiple universities and the USDA.
George Kantor from Carnegie Mellon University presented on distributed sensing in horticultural environments. Sensor networks use self-contained nodes to wirelessly collect data like temperature and humidity from fields and relay it back to a central point. Robots can also map fields using laser scanning and cameras. While sensor networks have moderate spatial resolution but high temporal resolution with simple sensing, robots have high spatial resolution but lower temporal resolution with more sophisticated sensing. Both approaches can be combined, with sensor networks monitoring fields frequently and robots providing more detailed scans less often. The collected data can be used with models to automate irrigation scheduling based on predicted plant water usage.
This document summarizes developments in automation technologies for tree fruit production being researched by Penn State University and industry partners. Key areas of research include automated fruit transport and bin filling to reduce labor costs, monitoring systems for insect pests and plant stress, autonomous crop load scouting for timely management decisions, and reconfigurable vehicles that can perform multiple orchard tasks like spraying and harvesting. Field trials are also testing new high-density orchard training systems. The goal is to develop precision technologies that increase productivity and efficiency for specialty crop growers.
This document summarizes research on developing an autonomous sensing and positioning system for use with fruit production equipment to reduce labor costs. Labor is one of the biggest challenges for specialty crop industries, and thinning fruit is very labor intensive. Existing mechanized thinning equipment like the Darwin String Thinner has been shown to significantly reduce costs. The researchers aim to improve this equipment by adding sensing capabilities and autonomous controls to increase speed and efficiency. They are testing ultrasonic and laser sensors to map trees and precisely position the equipment. Their goal is to develop fully autonomous thinning equipment to further reduce labor needs.
- Surveys were conducted at fruit growing conventions in Pennsylvania and New York to assess needs, benefits, and obstacles regarding automation technologies for specialty crops.
- The top areas in need of technological advancement were harvesting, spraying, monitoring yield and quality, and plant/soil/water/nutrient status.
- Anticipated benefits of harvest assist technologies included increased worker productivity, reduced costs, and improved management of harvest operations. However, cost was seen as a major obstacle.
- Automated monitoring technologies for diseases, insects, and plant stress were viewed favorably if proven effective at improving precision and efficiency of management practices.
The document describes two passive approaches to filling bins with apples during harvest: a pneumatic self-adjusting apple distributor and an energy absorbing grate. The pneumatic distributor uses inflatable cylinders and a padded ramp to gently distribute apples into bins, but requires moving between bins. The energy absorbing grate uses rubber bands or foam balls on a rubber grate to absorb impact and distribute apples, performing better with singulated fruit. Field tests showed the prototypes worked better than expected but transport from trees remains a challenge.
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This was presented to Pennsylvania Tree Fruit growers in the summer of 2009. It is a brief overview of our research on developing sensor networks for tree fruit orchards
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Accurate GPS-free Positioning of Utility Vehicles for Specialty Agriculture
1. Accurate GPS-free Positioning of Utility Vehicles for Specialty Agriculture Jacqueline Libby Robotics Institute Carnegie Mellon University George Kantor Robotics Institute Carnegie Mellon University
2. USDA: CASC (Comprehensive Automation for Specialty Crops) Carnegie Mellon University Plant Science Automation Robotics Localization Outreach GIS Ag Economics
4. Why isn’t GPS enough? Cost Sub-meter accuracy prohibitively expensive Performance Line of sight to satellites occluded by trees New fruit wall structures Orientation complement GPS
5. Experimental Platform Drive-by-wire electric vehicle Brake and steering motors Internal sensors Wheel encoders External Sensors 2 Sick LMS 291 laser range scanners Ruggedized Dell laptop Receive data via Ethernet, USB Ground Truth Applanix POS 220 LV high-accuracy positioning system
15. Prediction Step: Dead Reckoning Given previous pose Steering and wheel encoder values Predict: current pose Assumptions: Bicycle model Error bad sensor data Wheel slip imperfect assumptions
27. Conclusions and Future Work Very close to sub-meter accuracy goal Weakness: landmark spacing too dense Ongoing work: Improve prediction step: laser scan matching Improve measurement step: natural features Turning at the end of the row Future work Remove mapping step (SLAM) Other low-cost sensors (IMU, low-cost GPS)
Editor's Notes
Here is a picture of the robotic vehicle we use in our work.You can see here that it is driving by itself, based on the navigation and control algorithms developed by our group.Goal: determine position and orientation of this vehicle to sub meter accuracy without GPSMotivation:Reliable and affordable technology for specialty agriculture.High accuracy GPS cost-prohibitive
Pos info so fundamental to these types of systemsthat we often forget why it is significant.for example, allows data collected by on-board sensors to be geo-registered into maps this plot shows 3d point clouds from our laser range scanners other sensors, such as temperature and humidity, could be registered in a similar mannerAll this information can be collated into a GIS databaseThese photographs show examples of devices that could be towed on such a vehicle.This is a mower, and this is a weedseeker.This weedseeker both detects weeks and then sprays them.You could imagine that the information on where a week was detected, and where spraying occurred could be registered into a GIS data base. This could be used not only for record management, But for decision making purposes For example, a manager could decide on targeted areas for where further weedseeking should be performed. By targeting these areas, valuable chemicals and resources can be conservedOptional Automated vehicles Follow paths to targeted areas Safety Reduce human exposure to chemicals Reduce worker fatigue
Line of sight to satellites is occluded by treesNot a problem in broad-acre crops, where GPS has been used successfully for many yearsEven in tall tree canopies, if tractors are tall enough, then signal interference not an issuebut for smaller utility vehicles, such as the one used in this work, vehicle must operate well below tree lineApple orchards, orange groves, almond groves: all examples of specialty agriculture where tall tree canopies would cause an issue for small vehiclesOther applications: environmental/biologicalForests, tree farmsi.e. monitoring carbon sequestrationNew fruit wall structures:Engineered to maximize light interception by the canopyRule out possibility of mounting antennas above the tree lineCost:GPS systems that provide sub-meter accuracy are prohibitively expensiveGPS gives position, but not orientation, of the vehicleAutomation: Needed for controlling turnsCollecting data: needed for determining the position of an object in the environment wrt the vehicleComplementary:In some applications, perhaps a cheap GPS can still be usedOut algorithms can complement this GPSProvide corrections when the GPS fails -> robustnessProvide orientation on top of positionMore accuracy
Just talk about internal/external********************************drive-by-wire electric vehicleBrake and steering motors can be controlled by eitherHuman operatorAutonomous commands from an on-board computerInternal sensors: measure properties internal to vehicleEncoders: measure distance traveled and steering angleExternal sensors: measure properties of the environment2 Sick LMS 291 laser scannersSend out horizontal fan of beams, 180 degrees, 1 deg intervalsMeasure range and bearing to surrounding objectsRigidly attached, angled 30 degRuggedized Dell LaptopReceiving data via Ethernet and USBApplanix POS 220 LV high-accuracy positioning systemProvides ground truthPosition estimate: a few centimentersOrientation: 0.05 degrees
Use sensors already on vehicle for other purposes:Wheel encoders provide measurements of vehicle motionAlready being used for controlWe use for prediction of relative position
Laser range scannersAlready being used for safetyWe use to measure range and bearing to landmarks at known locationsCorrect our estimate of the vehicle’s pose
Knowing the locations of the landmarks requires having a map We create the map offline with the help of ground truth form the applanixThis is done offline
The main part of the algorithm is this feedback loop here.The wheel encoders provide a prediction of the vehicle’s position, which provides the dead reckoning step, in this boxThe lasers detect features in the map, which are used to correct this prediction which provides the correction step hereThe filter is then an iterative process that loops through this cycle as the vehicle moves through the world.
List of (x,y) positions of landmarks in the environmentReflective tape, lasers read as high-reflectivity returnsDrive through the orchard, passing by landmarks multiple times from different anglesRecording data from both lasers and applanix1) We start with the a range and bearing measurement to a landmark2) With then re-write this as the x,y position wrt the laser frame3) The “H’s” shown in the diagram are what we call homogeneous transformation matricesAllow us to transform (x,y) coordinates from one frame to anotherLaser frame -> vehicle frameVehicle frame -> world frame4) Laser -> vehicle: fixed position and orientation – we calibrate for this5) vehicle -> world: applanixAs we drive up and down the orchard rows, we record the world coordinates of all the high-refl returns,As you can see here in this map with the pink dotsWe then use a clustering technique to take each local point cloud region and turn it into a single point feature for the mapShown by the x’s
List of (x,y) positions of landmarks in the environmentReflective tape, lasers read as high-reflectivity returnsDrive through the orchard, passing by landmarks multiple times from different anglesRecording data from both lasers and applanix1) We start with the a range and bearing measurement to a landmark2) With then re-write this as the x,y position wrt the laser frame3) The “H’s” shown in the diagram are what we call homogeneous transformation matricesAllow us to transform (x,y) coordinates from one frame to anotherLaser frame -> vehicle frameVehicle frame -> world frame4) Laser -> vehicle: fixed position and orientation – we calibrate for this5) vehicle -> world: applanixAs we drive up and down the orchard rows, we record the world coordinates of all the high-refl returns,As you can see here in this map with the pink dotsWe then use a clustering technique to take each local point cloud region and turn it into a single point feature for the mapShown by the x’s
given:Vehicle pose at time tSteering and wheel encoder values at time t+TFind:Vehicle pose at time t+TAssumption:Ackerman steeringDifferential drive 4-wheel vehicleSimplify to bicycle modelEuler approximation: curved -> point and shootEncoder on motor gives estimate of the forward velocityEncoder on the steering wheels gives us an estimate of direction the vehicle is traveling in -> angular velocity
ReasonDead reckoning builds up error over timeUse measurements to landmarks to correct for this errorGiven:Actual measurement: range and bearing readings from laserModel of the measurement: Estimated vehicle (and laser) poseLandmark location, in mapLook at difference between actual measurement and measurement modelUse this to correct from our estimated laser pose closer to the actual laser pose
ReasonDead reckoning builds up error over timeUse measurements to landmarks to correct for this errorGiven:Actual measurement: range and bearing readings from laserModel of the measurement: Estimated vehicle (and laser) poseLandmark location, in mapLook at difference between actual measurement and measurement modelUse this to correct from our estimated laser pose closer to the actual laser pose
ReasonDead reckoning builds up error over timeUse measurements to landmarks to correct for this errorGiven:Actual measurement: range and bearing readings from laserModel of the measurement: Estimated vehicle (and laser) poseLandmark location, in mapLook at difference between actual measurement and measurement modelUse this to correct from our estimated laser pose closer to the actual laser pose
ReasonDead reckoning builds up error over timeUse measurements to landmarks to correct for this errorGiven:Actual measurement: range and bearing readings from laserModel of the measurement: Estimated vehicle (and laser) poseLandmark location, in mapLook at difference between actual measurement and measurement modelUse this to correct from our estimated laser pose closer to the actual laser pose
ReasonDead reckoning builds up error over timeUse measurements to landmarks to correct for this errorGiven:Actual measurement: range and bearing readings from laserModel of the measurement: Estimated vehicle (and laser) poseLandmark location, in mapLook at difference between actual measurement and measurement modelUse this to correct from our estimated laser pose closer to the actual laser pose
ReasonDead reckoning builds up error over timeUse measurements to landmarks to correct for this errorGiven:Actual measurement: range and bearing readings from laserModel of the measurement: Estimated vehicle (and laser) poseLandmark location, in mapLook at difference between actual measurement and measurement modelUse this to correct from our estimated laser pose closer to the actual laser pose
Iterative processPrediction and update steps coming in at different timesDependent on the frequencies of the various sensorsNot going into details here because not enough time….
This localization algorithm was tested successfully in a variety of settingsSite description:The plot here shows results from experiments in apple orchard blocks at the Penn State Fruit Research and Extension CenterDuring the summer of 2009Total area of block: 60 square m6 50 m long rowsOrchard block was relatively new planting (~3yr old), trained in a vertical trellis systemExperimental SetupTraffic cones were placed throughout the orchardA total of 39 landmarks, shows as black dotsSpaced about 20 m apart in pairsFirst collect a data set to create the map, which was constructed offlineSubsequent experiments run to test the localization algorithm onlineGenerated real-time position estimates as the vehicle drove through the blockAfter the tests, results were analyzed to generate statistics on performancePlotResults of a typical runExplain colorsVeering slightly offWhen the vehicle goes for a fair amount of time without receiving measurements to landmarksWhen the measurement is finally received, the vehicle corrects itselfHistogramPrimary metric we use to quantify error is the euclidean distance between the estimate and ground truth at each timestepMean: 20cm, max: 1.2 mThe cumulative error distribution is plotted in red, with the scale on the rightThis shows us, for instance, that 90% of the time, the error is under 35 cm
This localization algorithm was tested successfully in a variety of settingsSite description:The plot here shows results from experiments in apple orchard blocks at the Penn State Fruit Research and Extension CenterDuring the summer of 2009Total area of block: 60 square m6 50 m long rowsOrchard block was relatively new planting (~3yr old), trained in a vertical trellis systemExperimental SetupTraffic cones were placed throughout the orchardA total of 39 landmarks, shows as black dotsSpaced about 20 m apart in pairsFirst collect a data set to create the map, which was constructed offlineSubsequent experiments run to test the localization algorithm onlineGenerated real-time position estimates as the vehicle drove through the blockAfter the tests, results were analyzed to generate statistics on performancePlotResults of a typical runExplain colorsVeering slightly offWhen the vehicle goes for a fair amount of time without receiving measurements to landmarksWhen the measurement is finally received, the vehicle corrects itselfHistogramPrimary metric we use to quantify error is the euclidean distance between the estimate and ground truth at each timestepMean: 20cm, max: 1.2 mThe cumulative error distribution is plotted in red, with the scale on the rightThis shows us, for instance, that 90% of the time, the error is under 35 cm
This localization algorithm was tested successfully in a variety of settingsSite description:The plot here shows results from experiments in apple orchard blocks at the Penn State Fruit Research and Extension CenterDuring the summer of 2009Total area of block: 60 square m6 50 m long rowsOrchard block was relatively new planting (~3yr old), trained in a vertical trellis systemExperimental SetupTraffic cones were placed throughout the orchardA total of 39 landmarks, shows as black dotsSpaced about 20 m apart in pairsFirst collect a data set to create the map, which was constructed offlineSubsequent experiments run to test the localization algorithm onlineGenerated real-time position estimates as the vehicle drove through the blockAfter the tests, results were analyzed to generate statistics on performancePlotResults of a typical runExplain colorsVeering slightly offWhen the vehicle goes for a fair amount of time without receiving measurements to landmarksWhen the measurement is finally received, the vehicle corrects itselfHistogramPrimary metric we use to quantify error is the euclidean distance between the estimate and ground truth at each timestepMean: 20cm, max: 1.2 mThe cumulative error distribution is plotted in red, with the scale on the rightThis shows us, for instance, that 90% of the time, the error is under 35 cm
Scan to scan matching***********We have demonstrated sub-meter accuracy with current experimentsPrimary weakness: landmark spacing denser than is practicalWhen landmarks placed > 20m apart, dead reckoning becomes too largeAlgorithm will associate measurements to the wrong landmarkOngoing workImproving prediction stepLaser scan matching, more accurate dead reckoning, reducing driftImproving measurement stepNatural features:Entire tree rows: line featuresTree trunks: point featuresHard problem: turning at the end of rowWheel slipLaser odometry and better feature extractionFuture Work:SLAM (Simultaneous Localization and Mapping)No longer need expensive ground truthing for mapping stepOther low-cost sensors IMU’sPartial GPS EKF is a general sensor fusion techniquesLends itself to fusing data from multiple sensorsBy using multiple cheap sensors, we can provide affortable and reliable systems