Realtime Color Stereovision Processing

Recent developments in aviation have made micro air vehicles (MAVs) a reality. These featherweight palm-sized radio-controlled flying saucers embody the future of air-to-ground combat. No one has ever successfully implemented an autonomous control system for MAVs. Because MAVs are physically small with limited energy supplies, video signals offer superiority over radar for navigational applications. This research takes a step forward in real time machine vision processing. It investigates techniques for implementing a real time stereovision processing system using two miniature color cameras. The effects of poor-quality optics are overcome by a robust algorithm, which operates in real time and achieves frame rates up to 10 fps in ideal conditions. The vision system implements innovative work in the following five areas of vision processing: fast image registration preprocessing, object detection, feature correspondence, distortion-compensated ranging, and multi scale nominal frequency-based object recognition. Results indicate that the system can provide adequate obstacle avoidance feedback for autonomous vehicle control. However, typical relative position errors are about 10%-to high for surveillance applications. The range of operation is also limited to between 6 - 30 m. The root of this limitation is imprecise feature correspondence: with perfect feature correspondence the range would extend to between 0.5 - 30 m. Stereo camera separation limits the near range, while optical resolution limits the far range. Image frame sizes are 160x120 pixels. Increasing this size will improve far range characteristics but will also decrease frame rate. Image preprocessing proved to be less appropriate than precision camera alignment in this application. A proof of concept for object recognition shows promise for applications with more precise object detection. Future recommendations are offered in all five areas of vision processing

Random Image Matching CAPTCHA System

Security risks is an important issues and caught the attention of researchers in the area of networks, web development, human computer interaction and software engineering. One main challenge for online systems is to identify whether the users are humans or software robots (bots). While it is natural to provide service to human users, providing service for software robots (bots) comes with many security risks and challenges. Software robots are often used by spammers to create fake online accounts, affect search engine ranking, take part in on-line polls, send out spam or simply waste the resources of the server. In this paper we introduce a visual CAPTCHA technique that is based on generating random images by the computer, theuser is then asked to match a feature point between two images (i.e. solve the correspondence problem as defined by the researchers in the computer vision area). The relationship between the two images is based on a randomly generated homography transformation function. The main advantage of our approach compared to other visual CAPTCHA techniques is that we eliminate the need for a database of images while retaining ease of use

Safe Adaptive Traversability Learning for Mobile Robots

katedra kybernetik

Visually guided obstacle detection and avoidance for legged robot.

Chow Ying-ho.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 78-83).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Objectives - Visual Navigation for Legged Robots --- p.1Chapter 1.2 --- Summary of Results --- p.3Chapter 1.3 --- Hardware Issues --- p.4Chapter 1.4 --- Contributions --- p.4Chapter 1.5 --- Organization of the Thesis --- p.4Chapter Chapter 2 --- Previous Work --- p.6Chapter 2.1 --- Vision Based Navigation --- p.6Chapter 2.1.1 --- Homography --- p.7Chapter 2.1.2 --- Ground Plane Obstacle Detection --- p.9Chapter 2.1.3 --- Regression --- p.12Chapter 2.2 --- Control Strategy --- p.13Chapter Chapter 3 --- System Overview --- p.16Chapter Chapter 4 --- Obstacle Detection by Fast Homography Estimation --- p.20Chapter 4.1 --- Ground Feature Extraction --- p.21Chapter 4.2 --- Ground Feature Correspondence --- p.21Chapter 4.3 --- Ground Homography Estimation --- p.24Chapter 4.3.1 --- Input point transformation --- p.24Chapter 4.3.2 --- Initial estimation --- p.26Chapter 4.3.3 --- Robust estimation --- p.27Chapter 4.4 --- Obstacle Detection --- p.29Chapter 4.5 --- Local Obstacle Map (LOM) on Ground --- p.33Chapter 4.5.1 --- Extraction from accumulative evidence --- p.34Chapter 4.5.2 --- Time-delay compensation --- p.34Chapter Chapter 5 --- Obstacle Avoidance by a Fuzzy Controller --- p.36Chapter 5.1 --- Gait Pattern --- p.38Chapter 5.2 --- Fuzzy Logic Controller --- p.42Chapter 5.2.1 --- Controller Inputs --- p.42Chapter 5.2.2 --- Controller Outputs --- p.43Chapter 5.2.3 --- Inference mechanism --- p.46Chapter Chapter 6 --- Implementation --- p.49Chapter 6.1 --- Hardware components --- p.49Chapter 6.1.1 --- VisionBug --- p.49Chapter 6.1.2 --- RF transmitter / receiver modules: --- p.52Chapter 6.2 --- Perception --- p.55Chapter 6.3 --- Image Calibration --- p.56Chapter 6.4 --- Motion Calibration: --- p.58Chapter 6.5 --- Software Programs --- p.66Chapter 6.5.1 --- Computational complexity --- p.68Chapter Chapter 7 --- Experimental Results --- p.69Chapter 7.1 --- Real Navigation Experiments --- p.70Chapter 7.2 --- Error Analysis of LOM --- p.73Chapter Chapter 8 --- Conclusion and future work --- p.7

Efficient stereo matching and obstacle detection using edges in images from a moving vehicle

Fast and robust obstacle detection is a crucial task for autonomous mobile robots. Current approaches for obstacle detection in autonomous cars are based on the use of LIDAR or computer vision. In this thesis computer vision is selected due to its low-power and passive nature. This thesis proposes the use of edges in images to reduce the required storage and processing. Most current approaches are based on dense maps, where all the pixels in the image are used, but this places a heavy load on the storage and processing capacity of the system. This makes dense approaches unsuitable for embedded systems, for which only limited amounts of memory and processing power are available. This motivates us to use sparse maps based on the edges in an image. Typically edge pixels represent a small percentage of the input image yet they are able to represent most of the image semantics. In this thesis two approaches for the use of edges to obtain disparity maps are proposed and one approach for identifying obstacles given edge-based disparities. The first approach proposes a modification to the Census Transform in order to incorporate a similarity measure. This similarity measure behaves as a threshold on the gradient, resulting in the identification of high gradient areas. The identification of these high gradient areas helps to reduce the search space in an area-based stereo-matching approach. Additionally, the Complete Rank Transform is evaluated for the first time in the context of stereo-matching. An area-based local stereo-matching approach is used to evaluate and compare the performance of these pixel descriptors. The second approach proposes a new approach for the computation of edge-disparities. Instead of first detecting the edges and then reducing the search space, the proposed approach detects the edges and computes the disparities at the same time. The approach extends the fast and robust Edge Drawing edge detector to run simultaneously across the stereo pair. By doing this the number of matched pixels and the required operations are reduced as the descriptors and costs are only computed for a fraction of the edge pixels (anchor points). Then the image gradient is used to propagate the disparities from the matched anchor points along the gradients, resulting in one-voxel wide chains of 3D points with connectivity information. The third proposed algorithm takes as input edge-based disparity maps which are compact and yet retain the semantic representation of the captured scene. This approach estimates the ground plane, clusters the edges into individual obstacles and then computes the image stixels which allow the identification of the free and occupied space in the captured stereo-views. Previous approaches for the computation of stixels use dense disparity maps or occupancy grids. Moreover they are unable to identify more than one stixel per column, whereas our approach can. This means that it can identify partially occluded objects. The proposed approach is tested on a public-domain dataset. Results for accuracy and performance are presented. The obtained results show that by using image edges it is possible to reduce the required processing and storage while obtaining accuracies comparable to those obtained by dense approaches

Axel Rover Tethered Dynamics and Motion Planning on Extreme Planetary Terrain

Some of the most appealing science targets for future exploration missions in our solar system lie in terrains that are inaccessible to state-of-the-art robotic rovers such as NASA's Opportunity, thereby precluding in situ analysis of these rich opportunities. Examples of potential high-yield science areas on Mars include young gullies on sloped terrains, exposed layers of bedrock in the Victoria Crater, sources of methane gas near Martian volcanic ranges, and stepped delta formations in heavily cratered regions. In addition, a recently discovered cryovolcano on Titan and frozen water near the south pole of our own Moon could provide a wealth of knowledge to any robotic explorer capable of accessing these regions. To address the challenge of extreme terrain exploration, this dissertation presents the Axel rover, a two-wheeled tethered robot capable of rappelling down steep slopes and traversing rocky terrain. Axel is part of a family of reconfigurable rovers, which, when docked, form a four-wheeled vehicle nicknamed DuAxel. DuAxel provides untethered mobility to regions of extreme terrain and serves as an anchor support for a single Axel when it undocks and rappels into low-ground. Axel's performance on extreme terrain is primarily governed by three key system components: wheel design, tether control, and intelligent planning around obstacles. Investigations in wheel design and optimizing for extreme terrain resulted in the development of grouser wheels. Experiments demonstrated that these grouser wheels were very effective at surmounting obstacles, climbing rocks up to 90% of the wheel diameter. Terramechanics models supported by experiments showed that these wheels would not sink excessively or become trapped in deformable terrain. Predicting tether forces in different configurations is also essential to the rover's mobility. Providing power, communication, and mobility forces, the tether is Axel's lifeline while it rappels steep slopes, and a cut, abraded, or ruptured tether would result in an untimely end to the rover's mission. Understanding tether forces are therefore paramount, and this thesis both models and measures tension forces to predict and avoid high-stress scenarios. Finally, incorporating autonomy into Axel is a unique challenge due to the complications that arise during tether management. Without intelligent planning, rappelling systems can easily become entangled around obstacles and suffer catastrophic failures. This motivates the development of a novel tethered planning algorithm, presented in this thesis, which is unique for rappelling systems. Recent field experiments in natural extreme terrains on Earth demonstrate the Axel rover's potential as a candidate for future space operations. Both DuAxel and its rappelling counterpart are rigorously tested on a 20 meter escarpment and in the Arizona desert. Through analysis and experiments, this thesis provides the framework for a new generation of robotic explorers capable of accessing extreme planetary regions and potentially providing clues for life beyond Earth.</p

Generation of a Land Cover Atlas of environmental critic zones using unconventional tools

L'abstract è presente nell'allegato / the abstract is in the attachmen

The global tree carrying capacity (keynote)

editorial reviewe