INTELLIGENT VISION-BASED NAVIGATION SYSTEM

This thesis presents a complete vision-based navigation system that can plan and follow an obstacle-avoiding path to a desired destination on the basis of an internal map updated with information gathered from its visual sensor. For vision-based self-localization, the system uses new floor-edges-specific filters for detecting floor edges and their pose, a new algorithm for determining the orientation of the robot, and a new procedure for selecting the initial positions in the self-localization procedure. Self-localization is based on matching visually detected features with those stored in a prior map. For planning, the system demonstrates for the first time a real-world application of the neural-resistive grid method to robot navigation. The neural-resistive grid is modified with a new connectivity scheme that allows the representation of the collision-free space of a robot with finite dimensions via divergent connections between the spatial memory layer and the neuro-resistive grid layer. A new control system is proposed. It uses a Smith Predictor architecture that has been modified for navigation applications and for intermittent delayed feedback typical of artificial vision. A receding horizon control strategy is implemented using Normalised Radial Basis Function nets as path encoders, to ensure continuous motion during the delay between measurements. The system is tested in a simplified environment where an obstacle placed anywhere is detected visually and is integrated in the path planning process. The results show the validity of the control concept and the crucial importance of a robust vision-based self-localization process

Indoor mobile robot navigation with continuous localization.

by Lam Chin Hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 60-64).Abstracts in English and Chinese.Acknowledgments --- p.iiList of Figures --- p.vList of Tables --- p.viiAbstract --- p.viiiChapter 1 --- Introduction --- p.1Chapter 2 --- Algorithm Outline --- p.7Chapter 2.1 --- Assumptions --- p.7Chapter 2.2 --- Robot Localization --- p.8Chapter 2.3 --- Algorithm Outline --- p.11Chapter 3 --- Global and Local Maps --- p.15Chapter 3.1 --- Feature Selection --- p.17Chapter 3.2 --- Line Correspondence --- p.18Chapter 3.3 --- Map Representation --- p.20Chapter 3.3.1 --- Global Map --- p.21Chapter 3.3.2 --- Local Map --- p.22Chapter 3.4 --- Integration of Multiple Local 2D Maps --- p.24Chapter 4 --- Localization Algorithm --- p.27Chapter 4.1 --- Robot Orientation --- p.28Chapter 4.2 --- Robot Position --- p.29Chapter 4.2.1 --- Match Function --- p.30Chapter 4.2.2 --- Search Algorithm --- p.31Chapter 4.3 --- Continuous Localization with Retroactive Pose Update --- p.32Chapter 5. --- Implementation and Experiments --- p.35Chapter 5.1 --- Computing Robot Orientation --- p.36Chapter 5.2 --- Robot Position by Map Registration --- p.42Chapter 5.2.1 --- Error Analysis --- p.47Chapter 5.3 --- Discussions --- p.49Chapter 6. --- Conclusion --- p.52Appendix --- p.54Chapter A.l --- Intrinsic and Extrinsic Parameters --- p.54Chapter A.2 --- Relation Between Cameras (Stereo Camera Calibration) --- p.55Chapter A.3 --- Wheel-Eyes Calibration --- p.56Chapter A.4 --- Epipolar Geometry --- p.58Chapter A.5 --- The Tele-operate Interface --- p.59References --- p.6

E-Learning: Case Studies in Web-Controlled Devices and Remote Manipulation

Chances are that distance learning will transparently extend colleges and institutes of education and could plausibly overtake and turn into a preferred choice of higher education, especially for adult and working students. The main idea in e-learning is to build adequate solutions that can assure educational training over the Internet, without requiring a personal presence at the degree offering institution. The advantages are immediate and of unique importance, to enumerate a few: Education costs can be reduced dramatically, both from a student's perspective and the institution's (no need for room and board, for example); The tedious immigration and naturalization issues common with international students are eliminated; The limited campus facilities, faculty members and course schedules an institution can offer are no longer a boundary; Working adults can consider upgrading skills without changing their lifestyles We are presenting through this material a sequence of projects developed at University of Bridgeport and than can serve well in distance learning education ranging from simple "hobby" style training to professional guidance material. The projects have an engineering / laboratory flavor and are being presented in an arbitrary order, topics ranging from vision and sensing to engineering design, scheduling, remote control and operation

Case Studies in Web-Controlled Devices and Remote Manipulation

The concept of distance learning has been more and more articulated during the past few years and is expected to shortly turn into a practical education system within current high level learning institutions. The chances are that distance learning would transparently extend colleges and institutes of education, and could plausibly overtake and turn into a preferred choice of higher education, especially for adult and working students. The concept would be unachievable without the current technology, for example, the impressive worldwide accessibility of the Internet. The main idea in e-learning is to build adequate solutions that could assure educational training over the Internet, without requiring a personal presence at the degree offering institution. For example, being able to obtain a Bachelor’s degree in Computer Engineering from an accredited institution while residing thousands of miles away from it and actually never seeing it, except maybe for the graduation ceremony. The advantages are immediate and of unique importance, to enumerate a few: Scholarship / education costs can be reduced dramatically, both from a student’s perspective and the institution’s (no need for room and board, for example); The usually tedious immigration and naturalization issues that are common with international students are eliminated; The limited campus facilities, faculty members and course schedules an institution can offer are no longer a boundary; Working adults can consider upgrading skills without changing their lifestyle

An Incremental Navigation Localization Methodology for Application to Semi-Autonomous Mobile Robotic Platforms to Assist Individuals Having Severe Motor Disabilities.

In the present work, the author explores the issues surrounding the design and development of an intelligent wheelchair platform incorporating the semi-autonomous system paradigm, to meet the needs of individuals with severe motor disabilities. The author presents a discussion of the problems of navigation that must be solved before any system of this type can be instantiated, and enumerates the general design issues that must be addressed by the designers of systems of this type. This discussion includes reviews of various methodologies that have been proposed as solutions to the problems considered. Next, the author introduces a new navigation method, called Incremental Signature Recognition (ISR), for use by semi-autonomous systems in structured environments. This method is based on the recognition, recording, and tracking of environmental discontinuities: sensor reported anomalies in measured environmental parameters. The author then proposes a robust, redundant, dynamic, self-diagnosing sensing methodology for detecting and compensating for hidden failures of single sensors and sensor idiosyncrasies. This technique is optimized for the detection of spatial discontinuity anomalies. Finally, the author gives details of an effort to realize a prototype ISR based system, along with insights into the various implementation choices made

Localization of Non-Linearly Modeled Autonomous Mobile Robots Using Out-of-Sequence Measurements

This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost

Mapping by Cooperative Mobile Robots.

Constructing a system of intelligent robotic mapping agents that can function in an unstructured and unknown environment is a challenging task. With the exploration of our solar system as well as our own planet requiring more robust mapping agents, and with the drastic drop in the price of technology versus the gains in performance, robotic mapping is becoming a focus of research like never before. Efforts are underway to send mobile robots to map bodies within our solar system. While much of the research in robotic map construction has been focused on building maps used by the robotic agents themselves, very little has been done in building maps usable by humans. And yet it is the human that drives the need for mapping solutions. We propose a computational framework for building mobile robotic mapping systems to be deployed in unknown environments. This is the first work known to address the general problem of mapping in unknown terrain under the affect of error in readings, operations and systems that employs more than a single robot. The system draws upon the strengths from research in various robotic related areas by selecting those components and ideas that show promise when applied to mapping for human reading via a distributed network of heterogeneous mobile robots. This application of multiple mobile robots and the application to human end-users is a new direction in robotics research. We also propose and develop a new paradigm for storing mapping-agent generated data in a way that allows rapid map construction and correction to compensate for detected errors. We experimentally test the paradigm on a simulated robotic environment and analyze the results and show that there is a definite gain from correction, particularly in error rich environments. We also develop methods by which to apply corrections to the map and test their effectiveness. Finally we propose some extensions to this work and suggest research in areas not completely covered by our discussion

Non-Parametric Learning for Monocular Visual Odometry

This thesis addresses the problem of incremental localization from visual information, a scenario commonly known as visual odometry. Current visual odometry algorithms are heavily dependent on camera calibration, using a pre-established geometric model to provide the transformation between input (optical flow estimates) and output (vehicle motion estimates) information. A novel approach to visual odometry is proposed in this thesis where the need for camera calibration, or even for a geometric model, is circumvented by the use of machine learning principles and techniques. A non-parametric Bayesian regression technique, the Gaussian Process (GP), is used to elect the most probable transformation function hypothesis from input to output, based on training data collected prior and during navigation. Other than eliminating the need for a geometric model and traditional camera calibration, this approach also allows for scale recovery even in a monocular configuration, and provides a natural treatment of uncertainties due to the probabilistic nature of GPs. Several extensions to the traditional GP framework are introduced and discussed in depth, and they constitute the core of the contributions of this thesis to the machine learning and robotics community. The proposed framework is tested in a wide variety of scenarios, ranging from urban and off-road ground vehicles to unconstrained 3D unmanned aircrafts. The results show a significant improvement over traditional visual odometry algorithms, and also surpass results obtained using other sensors, such as laser scanners and IMUs. The incorporation of these results to a SLAM scenario, using a Exact Sparse Information Filter (ESIF), is shown to decrease global uncertainty by exploiting revisited areas of the environment. Finally, a technique for the automatic segmentation of dynamic objects is presented, as a way to increase the robustness of image information and further improve visual odometry results

Non-Parametric Learning for Monocular Visual Odometry

This thesis addresses the problem of incremental localization from visual information, a scenario commonly known as visual odometry. Current visual odometry algorithms are heavily dependent on camera calibration, using a pre-established geometric model to provide the transformation between input (optical flow estimates) and output (vehicle motion estimates) information. A novel approach to visual odometry is proposed in this thesis where the need for camera calibration, or even for a geometric model, is circumvented by the use of machine learning principles and techniques. A non-parametric Bayesian regression technique, the Gaussian Process (GP), is used to elect the most probable transformation function hypothesis from input to output, based on training data collected prior and during navigation. Other than eliminating the need for a geometric model and traditional camera calibration, this approach also allows for scale recovery even in a monocular configuration, and provides a natural treatment of uncertainties due to the probabilistic nature of GPs. Several extensions to the traditional GP framework are introduced and discussed in depth, and they constitute the core of the contributions of this thesis to the machine learning and robotics community. The proposed framework is tested in a wide variety of scenarios, ranging from urban and off-road ground vehicles to unconstrained 3D unmanned aircrafts. The results show a significant improvement over traditional visual odometry algorithms, and also surpass results obtained using other sensors, such as laser scanners and IMUs. The incorporation of these results to a SLAM scenario, using a Exact Sparse Information Filter (ESIF), is shown to decrease global uncertainty by exploiting revisited areas of the environment. Finally, a technique for the automatic segmentation of dynamic objects is presented, as a way to increase the robustness of image information and further improve visual odometry results