Vision Based Robot Localization Using Artificial And Natural Landmarks

In mobile robot applications, it is an important issue for a robot to know where it is. Accurate localization becomes crucial for navigation and map building applications because both route to follow and positions of the objects to be inserted into the map highly depend on the position of the robot in the environment. For localization, the robot uses the measurements that it takes by various devices such as laser rangefinders, sonars, odometry devices and vision. Generally these devices give the distances of the objects in the environment to the robot and proceesing these distance information, the robot finds its location in the environment. In this thesis, two vision-based robot localization algorithms are implemented. The first algorithm uses artificial landmarks as the objects around the robot and by measuring the positions of these landmarks with respect to the camera system, the robot locates itself in the environment. Locations of these landmarks are known. The second algorithm instead of using artificial landmarks, estimates its location by measuring the positions of the objects that naturally exist in the environment. These objects are treated as natural landmarks and locations of these landmarks are not known initially. A three-wheeled robot base on which a stereo camera system is mounted is used as the mobile robot unit. Processing and control tasks of the system is performed by a stationary PC. Experiments are performed on this robot system. The stereo camera system is the measurement device for this robot.

This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.

The use of vision in mobile robotics in one of the main goal of this thesis. In particular novel appearance based approaches for image matching metric are introduced. These approaches are applied to the problem of mobile robot localization.Similarity measures between robot's views are used in probabilistic methods for robot pose estimation. In this field of probabilistic localization active approach are proposed allowing the robot to faster and better localize. All methods have been extensively tested using a real robot in an indoor environment.Note: the book is the publication of the PhD thesis discussed in Università Politecnica delle Marche, Ancona, Italy in 2006 by Emanuele Frontoni

"Mobile robotics is an active and exciting sub-field of Computer Science. Its importance is easily witnessed in a variety of undertakings from DARPA's Grand Challenge to NASA's Mars exploration program. The field is relatively young, and still many challenges face roboticists across the boards. One important area of research is localization, which concerns itself with granting a robot the ability to discover and continually update an internal representation of its position. Vision based sensor systems have been investigated, but to much lesser extent than other popular techniques. A custom mobile platform has been constructed on top of which a monocular vision based localization system has been implemented. The rigorous gathering of empirical data across a large group of parameters germane to the problem has led to various findings about monocular vision based localization and the fitness of the custom robot platform. The localization component is based on a probabilistic technique called Monte-Carlo Localization (MCL) that tolerates a variety of different sensors and effectors, and has further proven to be adept at localization in diverse circumstances. Both a motion model and sensor model that drive the particle filter at the algorithm's core have been carefully derived. The sensor model employs a simple correlation process that leverages color histograms and edge detection to filter robot pose estimations via the on board vision. This algorithm relies on image matching to tune position estimates based on a priori knowledge of its environment in the form of a feature library. It is believed that leveraging different computationally inexpensive features can lead to efficient and robust localization with MCL. The central goal of this thesis is to implement and arrive at such a conclusion through the gathering of empirical data. Section 1 presents a brief introduction to mobile robot localization and robot architectures, while section 2 covers MCL itself in more depth. Section 3 elaborates on the localization strategy, modeling and implementation that forms the basis of the trials that are presented toward the end of that section. Section 4 presents a revised implementation that attempts to address shortcomings identified during localization trials. Finally in section 5, conclusions are drawn about the effectiveness of the localization implementation and a path to improved localization with monocular vision is posited"--Abstract.

Effective robot navigation and route planning is impossible unless the position of the robot within its environment is known. Motion sensors that track the relative movement of a robot are inherently unreliable, so it is necessary to use cues from the external environment to periodically localize the robot. In this study I examine the feasibility of using the probabilistic Monte Carlo localization algorithm to estimate a robot's location based off of occasional visual landmark cues. To demonstrate this, I designed a robot capable of localizing within Olin-Rice by observing pieces of colored paper placed at regular intervals along the halls.

SYROCO'2003 covered areas and aspects of robot control Topics: Robot control techniques (adaptive, robust, learning) Modeling and identification Control of discrete / continuous-time robotic systems Non-holonomic robotic systems Intelligent control Control based on sensing Control design and architectures Force and compliance control Grasp control Flexible robots Micro robots Mobile robots Walking robots Humanoid robots Teleoperation and man / machine dynamic systems Multi-Robot-Systems, cooperative robots Applications: space, underwater, civil engineering, surgery, entertainment, mining, etc. *Provides the latest research on Robotics *Contains contributions written by experts in the field. *Part of the IFAC Proceedings Series which provides a comprehensive overview of the major topics in control engineering.

This book constitutes the refereed proceedings of the 9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2013, held in Paphos, Cyprus, in September/October 2013. The 26 revised full papers presented together with a keynote speech at the main event and 44 papers of 8 collocated workshops were carefully reviewed and selected for inclusion in the volume. The papers of the main event are organized in topical sections on data mining, medical informatics and biomedical engineering, problem solving and scheduling, modeling and decision support systems, robotics, and intelligent signal and image processing.