This document describes an automated sorting machine that uses video processing and a robotic arm. The machine aims to decrease production costs and time by automating the sorting of two classes of objects. It does this through three stages: 1) image processing algorithms to acquire images and label each object, 2) controlling the arm and mapping the background for localization, and 3) interfacing the computer vision and robotic arm so the arm can automate sorting based on the labeled objects. The system uses a camera, robotic arm, and microcontroller to pick up objects using inverse kinematics calculations and sort them into separate classes.
This project aims to create a rough map of an object using an ultrasonic sensor. The sensor measures distance at different points on the object as it is rotated and moved. These distance measurements are sent to a computer running MATLAB, which processes the data and generates a mesh plot approximating the object's shape. Components needed include an Arduino, ultrasonic sensor, servo motor, and lifting mechanism to move the sensor. Limitations include low precision and the inability to create a full 3D plot. Potential extensions involve adding remote control, improved filtering, and using it to map rooms in dark environments.
This document summarizes an oscilloscope application project for TinyOS. It describes:
1) How the mote senses and broadcasts data and the base station gets the data and plots it in real time using a Java application.
2) The implementation in OscilloscopeAppC.nc which includes components for sensing, timers, LEDs, and active messaging.
3) How to compile and run the application by setting the sensor board type and running the Java GUI after exporting the MOTECOM environment variable.
Handheld device motion tracking using MEMS gyros and accelerometeranusheel nahar
This document discusses using MEMS gyroscopes and accelerometers for motion tracking in gaming applications. It describes how gyroscopes measure angular velocity and accelerometers measure acceleration. Integration of gyroscope data causes drift over time, while accelerometer and magnetometer data can be fused to estimate orientation. The document proposes a method using a modified Kalman filter to fuse the sensor data and estimate orientation without boundless drift. Simulation results show the method accurately tracks motion while overcoming noise and drift issues better than a standard Kalman filter.
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This document compares real-world robotic manipulation tasks to simulations in various environments. It finds that no simulator perfectly matches the real world, but some perform better than others. It records motion capture ground truth data from a robotic arm performing reaching and object interaction tasks. The document then compares this data to simulations of the same tasks in MuJoCo, PyBullet, V-Rep, Bullet, Newton and Vortex. It analyzes the simulations' accuracy in replicating the robot's trajectory, object displacement and interactions. It finds MuJoCo and Bullet simulate interactions best but with inaccuracies, while Newton and Vortex best match the robot's trajectory but interact minimally with objects. Overall no simulator perfectly bridges the "reality gap"
In this MATLAB based project we are extracting the frames from the video and dividing each frame into two quadrant. After detecting the face in respective quadrant we are setting the flag variable. This flag variable later can be used as a switch for automatic controlling of the home appliances.
Structured light systems use camera-projector pairs to create 3D models. They must be calibrated by determining the intrinsic and extrinsic parameters of the cameras and projectors. The document describes a calibration technique that adapts Huang's method, which calibrates projectors similar to cameras, simplifying the process. The authors designed a quick and accurate calibration for their hardware that can be integrated into a multi-camera, projector system to simultaneously calibrate multiple pairs.
This document describes an automated sorting machine that uses video processing and a robotic arm. The machine aims to decrease production costs and time by automating the sorting of two classes of objects. It does this through three stages: 1) image processing algorithms to acquire images and label each object, 2) controlling the arm and mapping the background for localization, and 3) interfacing the computer vision and robotic arm so the arm can automate sorting based on the labeled objects. The system uses a camera, robotic arm, and microcontroller to pick up objects using inverse kinematics calculations and sort them into separate classes.
This project aims to create a rough map of an object using an ultrasonic sensor. The sensor measures distance at different points on the object as it is rotated and moved. These distance measurements are sent to a computer running MATLAB, which processes the data and generates a mesh plot approximating the object's shape. Components needed include an Arduino, ultrasonic sensor, servo motor, and lifting mechanism to move the sensor. Limitations include low precision and the inability to create a full 3D plot. Potential extensions involve adding remote control, improved filtering, and using it to map rooms in dark environments.
This document summarizes an oscilloscope application project for TinyOS. It describes:
1) How the mote senses and broadcasts data and the base station gets the data and plots it in real time using a Java application.
2) The implementation in OscilloscopeAppC.nc which includes components for sensing, timers, LEDs, and active messaging.
3) How to compile and run the application by setting the sensor board type and running the Java GUI after exporting the MOTECOM environment variable.
Handheld device motion tracking using MEMS gyros and accelerometeranusheel nahar
This document discusses using MEMS gyroscopes and accelerometers for motion tracking in gaming applications. It describes how gyroscopes measure angular velocity and accelerometers measure acceleration. Integration of gyroscope data causes drift over time, while accelerometer and magnetometer data can be fused to estimate orientation. The document proposes a method using a modified Kalman filter to fuse the sensor data and estimate orientation without boundless drift. Simulation results show the method accurately tracks motion while overcoming noise and drift issues better than a standard Kalman filter.
This document summarizes a research paper that proposes a new method called SeDDaRA (Self-deconvolving Data Reconstruction Algorithm) to improve solar imaging using shift-and-add (SAA) techniques. SeDDaRA first applies self-deconvolution to enhance high-frequency components in speckle images. It then uses SAA, choosing the reference frame based on the image with the highest root-mean-square contrast (RMSC). Finally, a second SAA is performed using the first result as the reference frame, producing the final high-resolution image. Figures in the document show example input/output images from this new two-step SAA process with self-deconvolution.
This document compares real-world robotic manipulation tasks to simulations in various environments. It finds that no simulator perfectly matches the real world, but some perform better than others. It records motion capture ground truth data from a robotic arm performing reaching and object interaction tasks. The document then compares this data to simulations of the same tasks in MuJoCo, PyBullet, V-Rep, Bullet, Newton and Vortex. It analyzes the simulations' accuracy in replicating the robot's trajectory, object displacement and interactions. It finds MuJoCo and Bullet simulate interactions best but with inaccuracies, while Newton and Vortex best match the robot's trajectory but interact minimally with objects. Overall no simulator perfectly bridges the "reality gap"
In this MATLAB based project we are extracting the frames from the video and dividing each frame into two quadrant. After detecting the face in respective quadrant we are setting the flag variable. This flag variable later can be used as a switch for automatic controlling of the home appliances.
Structured light systems use camera-projector pairs to create 3D models. They must be calibrated by determining the intrinsic and extrinsic parameters of the cameras and projectors. The document describes a calibration technique that adapts Huang's method, which calibrates projectors similar to cameras, simplifying the process. The authors designed a quick and accurate calibration for their hardware that can be integrated into a multi-camera, projector system to simultaneously calibrate multiple pairs.
Structure from motion is a computer vision technique used to recover the three-dimensional structure of a scene and the camera motion from a set of images. It involves detecting feature points in multiple images, matching corresponding points across images, estimating camera poses and orientations, and reconstructing the 3D geometry of scene points. Large-scale structure from motion can reconstruct scenes from thousands of images but requires solving very large optimization problems. Applications include 3D modeling, surveying, robot navigation, virtual reality, augmented reality, and simultaneous localization and mapping.
Structure from motion is a computer vision technique used to recover the three-dimensional structure of a scene and the camera motion from a set of images. It can be used to build 3D models of scenes without any prior knowledge of the camera parameters or 3D locations of the scene points. Structure from motion involves detecting feature points in multiple images, matching the features between images, estimating the fundamental matrices between image pairs, and then optimizing a bundle adjustment problem to simultaneously compute the 3D structure and camera motion parameters. Some applications of structure from motion include 3D modeling, surveying, robot navigation, virtual and augmented reality, and visual effects.
COSC 426 Lecture 5 on Mathematical Principles Behind AR Registration. Given by Adrian Clark from the HIT Lab NZ at the University of Canterbury, August 8, 2012
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Henrik Christensen - Vision for co-robot applicationsDaniel Huber
The document discusses a vision for co-robot applications where robots can work collaboratively with humans. It outlines challenges for perception tasks as robots move from controlled settings to unstructured environments. Specifically, challenges include handling objects with and without textures, dealing with background clutter, object discontinuities, and meeting real-time constraints. Approaches discussed include using 2D visual information from monocular cameras and 3D information from RGB-D cameras to estimate object poses for pick-and-place tasks.
Nadia Figueroa's document discusses 3D computer vision applications in robotics and multimedia. It first provides background on 3D computer vision and describes sensing devices like LIDAR, radar and sonar that are used. Applications in robotics discussed include object recognition, mapping and navigation for mobile manipulation platforms. Figueroa's master's thesis at DLR focused on developing a verification routine to identify positioning errors in the upper body kinematics of the humanoid Justin robot using an onboard stereo vision system and 3D point cloud registration.
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1. Estimate camera motion and 3D structure from a sequence of images using a stratified approach, starting with projective reconstruction and refining to affine and metric reconstruction.
2. Reconstruct structure and motion for two datasets - a public dataset and a personal dataset acquired by the student.
3. The key steps are feature detection, matching, estimating the fundamental matrix, triangulating 3D points, identifying the plane at infinity to upgrade from projective to affine reconstruction, and further refinement to metric reconstruction if possible.
Real-time 3D Object Pose Estimation and Tracking for Natural Landmark Based V...c.choi
1) The document describes a real-time method for estimating and tracking the 3D pose of a rigid object using either a mono or stereo camera.
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This document proposes a novel method for video saliency detection based on an adaptive nonlinear partial differential equation (PDE) model. The key contributions are:
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2. Combining static saliency maps generated from the PDE model with motion maps extracted from motion vectors to produce the final saliency map.
3. Extending the model to account for flow-like structures by adding a non-linear matrix tensor to rotate the PDE flow towards orientations of interesting features.
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Demo In Greek:
https://www.youtube.com/watch?v=5C_p7MHKA4g
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This document summarizes the key problems in robot localization and different estimation techniques. It discusses (1) dead reckoning using odometry, (2) using a map and observing known features, (3) creating a map, (4) simultaneous localization and mapping, and (5) Monte Carlo estimation techniques. The document focuses on using the Kalman filter and extended Kalman filter to provide optimal state estimates under Gaussian noise assumptions, and introduces particle filters as a method that makes no distribution assumptions.
The flow of baseline estimation using a single omnidirectional cameraTELKOMNIKA JOURNAL
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2. As the camera is moved horizontally, tracking points on an object in panoramic images produces coordinate shifts that are saved and represented as graphs.
3. Analyzing the graphs allows determining the equation that estimates the baseline flow and coefficients of the equation.
This document discusses a framework for one-shot 3D model learning of objects using a multi-view sensor setup to allow robots to learn new objects. A pose estimation algorithm first computes the pose from a single view and then refines it using all three views. The approach uses three stereo camera pairs and three Kinect sensors to ensure 360 degree coverage of an object. Models are reconstructed from merged point clouds using steps like filtering, clustering, registration, and reconstruction. Grasps are then computed and evaluated on the models to test if noisy learned models are suitable for grasp planning.
This document provides information about image reconstruction in multi-detector computed tomography (MDCT). It begins with an overview of the basic principles of CT imaging, including image formation steps and reconstruction methods. It then describes the principles of helical CT scanning and how this enables volumetric data acquisition. Finally, it discusses image reconstruction techniques for MDCT, including interpolation methods needed to reconstruct images from the helical scan data. In particular, it notes that multi-detector arrays allow acquisition of multiple slices with each rotation, significantly increasing scan speed and coverage compared to earlier single-detector row CT.
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3. The key steps are feature detection, matching, estimating the fundamental matrix, triangulating 3D points, identifying the plane at infinity to upgrade from projective to affine reconstruction, and further refinement to metric reconstruction if possible.
Real-time 3D Object Pose Estimation and Tracking for Natural Landmark Based V...c.choi
1) The document describes a real-time method for estimating and tracking the 3D pose of a rigid object using either a mono or stereo camera.
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The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
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A default auto-signature option and a refuse-to-sign option in HR modules.
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Virtual Dance Game Using Kinect and ICP Algorithm
1. THE MAGIC OF KINECT
CAPSTONE PROJECT
BENJOE VIDAL
2. WHAT THE KINECT DOES ?
• GET THE DEPTH IMAGE
• ESTIMATE THE BODY POSE
• OUTPUT ( APPLICATION)
3. HOW KINECT WORKS
• IR PROJECTOR
• PROJECTED LIGHT PATTER (SPECLE PATTERN - TIME OF
FLIGHT)
• IR SENSOR
• STEREO ALGORITHM - CONVERT TO DEPTH IMAGE
• SEGMENTATION
• DEPTH IMAGE
6. DEPTH IMAGE
• is constructed by analyzing a specle pattern of
infrared laser light.
• inferring body position is a two - stage process.
- First compute a depth map( using structured
light)
8. HOW KINECT GET THE IMAGE DATA
• A time-of-flight camera emits light signals and then measures how
long it takes them to return. That needs to be accurate to
1/10,000,000,000 of a second, the camera is able to differentiate
light reflecting from objects in a room and the surrounding
environment. That provides an accurate depth estimation that
enables the shape of those objects to be computed.
9. HOW WE INTEGRATE THE GESTURE RECOGNITION.
• We used OMEK - GAT to record gesture.
• Convert to XML data
• Pattern maching algorithm
• ICP - Iterative Closest Point
10.
11.
12. PATTERN MATCHING
• pattern maching involves having a database of recorded images
produced by gestures.
• Using this database you can compare the current drawing(made with
the positions of the tracked joint) with all the recorded drawings in
the database.
• Pattern matching algorithm, determines if one of the templates
matches the current drawing, and if so, then the gesture is detected.
13. ITERATIVE CLOSEST POINT
• The ICP (Iterative Closest Point) algorithm is widely used for
geometric alignment of three-dimensional models when an initial
estimate of the relative pose is known. This capability has potential
application to real-time 3D model acquisition and model-based
tracking.
14. • Iterative closest point algorithm to solve the
simultaneous assignment of silhouette points to a
body part and alignment of the body part.
15.
16. • Awake is used to initialize any variables or game state before the
game starts.
• Bool beckonAlive = BeckonManager.beckonInstance.IsInit(); // this
line initialize the beckon SDK
• OMKStatus rc; M_currentSkeleton = Factory.createSkeleton(out rc);
this line will create skeleton every point of joint of avatar. If the
OMKStatus is successfully initialized InitializeHeirarchy(); function
will call to create skeleton
17.
18. • after the skeleton successfully attached to the
avatar it will animate the movement of the avatar
same as the movement of the player in the game