Lecture 05
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This document discusses various computer vision techniques for obtaining range and depth information from images, including depth from focus, depth from stereo, and optical filters. It introduces the pinhole camera model and explains how depth can be estimated from a single image by analyzing focus versus from multiple images using stereo vision. Examples of image processing techniques like convolution and morphological operations are also provided. The challenges of object recognition are discussed, noting that perception depends on assumptions about lighting, geometry, frequency, color and other factors that can be ambiguous.
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Vision is a challenging task for robots due to various ambiguities, including: depth discontinuities, surface orientation changes, reflectance changes, illumination changes, and different perspectives. Basic image processing techniques can help with tasks like edge detection, thresholding, morphological operations, and stereo vision calculations, but object recognition still requires combining these techniques with filters tuned to the specific objects. For homework, students should read about these topics and complete a questionnaire.
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Both parallax barrier systems and the practical holographic displays are limited in that they only provide horizontal parallax. Mathematically, this is equivalent to saying that they can always be expressed as a rank-1 matrix (i.e, a matrix in which all the columns are linearly related). Knowledge of this mathematical limitation has helped us to explore the space of possibilities and extend the capabilities of current display types. In particular, we have designed a display that uses two LCD panels, and an optimisation algorithm, to produce a content-adaptive automultiscopic display (SIGGRAPH Asia 2010).
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In other work we have developed a 6D optical system that responds to changes in viewpoint as well as changes in surrounding light. Our lenticular array alignment allows us to achieve such a system as a passive setup, omitting the need for electrical components. Unlike traditional 2D flat displays, our 6D displays discretize the incident light field and modulate 2D patterns in order to produce super-realistic (2D) images. By casting light at variable intensities and angles onto our 6D displays, we can produce multiple images as well as store greater information capacity on a single 2D film (SIGGRAPH 2008).
Ramesh Raskar joined the Media Lab from Mitsubishi Electric Research Laboratories in 2008 as head of the Lab’s Camera Culture research group. His research interests span the fields of computational photography, inverse problems in imaging and human-computer interaction. Recent inventions include transient imaging to look around a corner, next generation CAT-Scan machine, imperceptible markers for motion capture (Prakash), long distance barcodes (Bokode), touch+hover 3D interaction displays (BiDi screen), low-cost eye care devices (Netra) and new theoretical models to augment light fields (ALF) to represent wave phenomena.
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For recording derivation just go through LASERS book by Milloni. It is very easy and useful derivation. Thank you.
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For recording derivation just go through LASERS book by Milloni. It is very easy and useful derivation. Thank you.
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Holographic projection technology allows for the creation and display of true three-dimensional images without the need for glasses. It records images as interference patterns of light and allows the image to be seen from different angles like a real object. The father of holography was Dennis Gabor, who first wrote about it in 1948 before the invention of lasers. Holographic displays can show high resolution 3D images that are glasses-free and interactive. Potential applications include entertainment, education, virtual communication, simulation, planning, and military and space uses. Holography is expected to become an important part of human civilization and communication.
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Lecture 06: Features and Uncertainty
1. The document discusses various topics related to robotics including vision, midterm exams, locomotion, control, mapping, and homework.
2. Key concepts that are reviewed include line detection, calculating uncertainty from sensor measurements, propagating uncertainty in features, and different algorithms for mapping like watershed segmentation.
3. Exercises are assigned related to writing controllers for different robots, dealing with obstacles, and improving control parameters. Students are divided into groups to work on a final project to implement a controller.
Lecture 10: Summary
This document provides a summary of the key topics covered in an introduction to robotics course over 15 weeks, including:
- Locomotion and kinematics concepts like dynamic and static stability, forward and inverse kinematics, and differential drive constraints.
- Sensors like laser range scanners, their properties and how to model uncertainty. Visual sensors, odometry, and localization using Bayes' rule and Kalman filters.
- Midterm and final exam preparation advice focusing on understanding core concepts rather than derivations, and practicing examples like kinematics and localization problems.
- The final exam is scheduled for Wednesday, December 15 from 7:30-10:00 pm in the CAETE classroom.
Showcase
Robots Alive is an Indian company established in 2009 that specializes in robotics and automation technologies. It has provided solutions to customers in various industries like manufacturing, FMCG, oil and lubricants, packaging, education, and healthcare. The company's vision is to enable everyday life with robots and become a thought leader in robotics. Its mission is to innovate, drive excellence, and continuously adapt to customer needs. Robots Alive develops and sells various types of robots including ones for in-mould labeling, industrial training, high-speed picking and packaging, CNC machining, and more.
Three-dimensional construction with mobile robots and modular blocks
Three-dimensional construction with mobile robots and modular blocksUniversity of Colorado at Boulder
Three-Dimensional Construction with Mobile Robots and Modular Blocks presents a system for constructing 3D structures using mobile robots and modular blocks. Passive blocks contain self-aligning connectors and communicate locally to maintain a "shape map" for construction. Active robots manipulate blocks and do not communicate. The system uses simple rules for block placement to reliably build any structure meeting criteria of no loops and limited overhangs. Various robot algorithms are evaluated for building structures with the lowest costs.Lecture 09: SLAM
1) Probabilistic state estimation techniques like the Kalman filter, extended Kalman filter, unscented Kalman filter, and particle filters allow estimating the state of a system using probabilistic models and sensor measurements with uncertainty.
2) The Kalman filter provides a recursive solution to the linear filtering problem by estimating the current state as a weighted average of the prior state and new measurements, with the weights based on their uncertainties.
3) Extensions to the Kalman filter like the extended and unscented Kalman filters allow handling nonlinear systems by linearizing around the current estimate, while particle filters represent the state distribution with random samples and are more flexible for nonlinear and non-Gaussian problems like SLAM.
Lecture 08: Localization and Mapping II
This document discusses algorithms for localization and mapping in robotics, including Dijkstra's algorithm, A* search, odometry, GPS, landmarks, and gyroscopes. It introduces probabilistic localization using Markov localization on discrete state spaces like grids or topological maps. Bayes' rule is used to update the probability of a robot's location based on sensor readings and motion. Particle filters are presented as a way to reduce complexity by random sampling rather than maintaining a full probability distribution over all locations. Examples show mapping and localization in a grid world using a particle filter approach. The homework assignment is to read about Markov localization and particle filters, and next week will include design reviews from student groups.
Lecture 03 - Kinematics and Control
This document provides an introduction to robotics kinematics including forward kinematics, inverse kinematics, and maneuverability. It discusses calculating a robot's speed given its wheel speeds and geometry using forward kinematics, and determining required wheel speeds to achieve a desired speed using inverse kinematics. It also covers a robot's degree of mobility based on the number of independently controllable wheels and degree of steerability based on the number of independently steered wheels to determine its overall maneuverability. Examples are provided and the document concludes with assigning homework to read about forward kinematics and program a waypoint following robot.
Lecture 07: Localization and Mapping I
This document discusses localization and mapping for robotics. It introduces topics like gyroscopes, odometry, GPS, and landmarks for localization. It discusses uncertainty models using Gaussian distributions and error propagation. Methods for belief representation are presented, including parametric single/multi hypothesis and non-parametric particle filters. Environment representations like continuous, discrete, and topological maps are described. The document provides an example of Google Maps and discusses belief representation in topological maps. It also covers multi-hypothesis belief representation, sensor data to topological maps using exact and Voronoi decompositions, and adaptive cell-size. The document assigns homework on navigation algorithms and reactive vs. deliberative planning.
Robot Manipulation Basics
Robot Manipulation is the basic science in Robotics. Most of the modern theories arise from the classical concepts of robot manipulation.
Robot Configuration - 1
This document discusses different types of industrial robots classified by their arm configuration, power source, and path control. It describes Cartesian robots which move along three orthogonal axes and have a rectangular working envelope. Cylindrical robots use a vertical column that can rotate and slide up/down, giving them a cylindrical working volume. Both robot types have advantages like payload capacity and work area, as well as disadvantages like limited movement directions and lower accuracy for cylindrical robots. Common applications include pick and place, assembly, welding, and machine loading/unloading.
Robot Configuration - 2
The document discusses different robot configurations including polar, jointed arm, and SCARA configurations. A polar configuration robot has a spherical work volume defined by one linear and two rotary motions. It has advantages of a larger work envelope and more compact size but lower accuracy. Jointed arm configurations resemble a human arm with rotary shoulder and elbow joints. SCARA robots are a type of jointed arm designed for horizontal insertion tasks.
Robotics by rk mittal
The document discusses the benefits of exercise for mental health. It notes that regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help alleviate symptoms of mental illness.
Industrial robotics
A robot is a mechanical device guided by a computer program capable of performing industrial tasks. Robots usually have a body, arm, and wrist and can use different coordinate systems like polar, cylindrical, or Cartesian. They are classified by their configuration, workspace shape, power source, and technology level. Robots vary in size and are specified by their pitch, yaw, roll, joint notation, speed, and payload.
Computer Science Engineering
Computer science is the study of computer hardware, software, and applications. It involves designing algorithms, programming computers, and working with microprocessors. The work of computer scientists falls into three categories: theory, design and analysis of algorithms, and experimental work. Computer science engineers work in many fields including schools, offices, industries, transportation and communication to computerize machines and technologies that make life easier. Career prospects for computer science engineers are rapidly increasing both in India and abroad due to high demand, with employment projected to grow 38% over the next decade.
Lecture 01: Introduction
This document provides an introduction and syllabus for a robotics course. It will cover topics like locomotion, perception, planning and navigation. Students will gain hands-on experience through simulation exercises and programming robots. They will also participate in an online robot soccer competition. The course aims to teach the science and challenges of robotics through both theoretical lectures and practical assignments using a robot simulation program called Webots.
Industrial robotics
This document provides an overview of industrial robotics, including robot anatomy, control systems, end effectors, applications, and programming. It describes the typical components of a robot like links, joints, drives, and sensors. Common robot configurations and their joint notation are shown. The document also discusses robot programming methods including leadthrough and textual languages, as well as simulation for offline programming.
Mechanical Engineering
Mechanical engineering involves understanding core concepts in mechanics, kinematics, thermodynamics, materials science, and structural analysis. Mechanical engineers design and analyze machines, manufacturing plants, engines, transport systems, medical devices, and more. Examples of mechanical engineering include bicycles, CD players, video game consoles, snowmobiles, microelectromechanical systems, robotics, composite materials, biomechanics, aerospace engineering, and careers in automotive, manufacturing, utilities, HVAC, and space research industries.
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Lecture 05
- 1. Introduction to RoboticsVision-based ranging and Optical Filters CSCI 4830/7000 September 27, 2010 NikolausCorrell
- 2. Review: Sensing Important: sensors report data in their own coordinate frame Examples from the exercise Accelerometer of Nao Laser scanner Treat like forward kinematics
- 4. Today Perception using vision Range information from Vision Basic Image Processing Why is object recognition hard? -> “Computer Vision” with Jane Mulligan
- 5. Range sensing Last week Laser scanner (phase shift) Infrared (path loss) Ultrasound (time-of-flight) Today Depth from focus Depth from Stereo
- 6. Pin-Hole Camera A. Efros
- 8. Aperture
- 9. Thin Lens Objects need to have the right distance to be in focus -> Depth-from-Focus method
- 10. Depth from Focus “in focus” + camera parameters = range How to test whether an image is “crisp” or “blurry”?
- 11. Testing for focus Unit Step -> 2nd Derivative Intuition: Images with high contrast have steep edges!
- 12. Convolution Calculate Laplacian / 2nd derivative by “convolving” image with 2D Kernel *
- 13. Depth from Stereo Distance between stereo pair known + distance in the image -> distance to object
- 14. Stereo Pairs Zero crossings of Laplacians of Gaussians Gaussians: blurred image (suppresses noise) Laplacians: edges Test how far similar edges are apart Epipolar constraints are given by the geometry of the Stereo pair
- 15. Other example for Convolutions: Canny Edge Detector 1. 2.+3. 4. Trace along ridges (non-maximum suppression) 15
- 16. Exercise: Thresholds 16 16 http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm Screen shots by Gary Bradski, 2005
- 17. Exercise: Morphological Operations Examples Morphology - applying Min-Max. Filters and its combinations Dilatation IB Opening IoB= (IB)B Erosion IB Image I Closing I•B= (IB)B TopHat(I)= I - (IB) BlackHat(I)= (IB) - I Grad(I)= (IB)-(IB)
- 19. Why is Object Recognition Hard?The difference between seeing and perception. Gary Bradski, 2009 19 What to do? Maybe we should try to find edges …. Gary Bradski, 2005
- 22. Reflectance discontinuity (i.e., change in surface material properties)
- 23. Illumination discontinuity (e.g., shadow)Slide credit: Christopher Rasmussen 20 But, What’s an Edge?
- 24. To Deal With the Confusion, Your Brain has Rules...That can be wrong
- 25. We even see invisible edges…
- 26. And surfaces …
- 27. We need to deal with 3D Geometry 24 Perception is ambiguous … depending on your point of view! Graphic by Gary Bradski
- 28. And Lighting in 3D Which square is darker?
- 29. Lighting is Ill-posed … Perception of surfaces depends on lighting assumptions 26 Gary Bradski (c) 2008 26
- 30. Contrast 27 Which one is male and which one is female? Illusion by: Richard Russell,Harvard University Russell, R. (2009) A sex difference in facial pigmentation and its exaggeration by cosmetics. Perception, (38)1211-1219
- 31. Frequency
- 33. Homework Read sections 4.2-5 (pages 145-180) Questionnaire on CU Learn Midterm: October 11 (during class)