Topic
Mobile robot navigation
About: Mobile robot navigation is a research topic. Over the lifetime, 14713 publications have been published within this topic receiving 263092 citations.
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Book•
01 Jan 1999
TL;DR: The Science of Navigation.
Abstract: The Science of Navigation. Coordinate Frames and Transformations. Systems Concepts. Discrete Linear and Non-Linear Kalman Filtering Techniques. The Global Positioning System. Inertial Navigation. Navigation Examples and Case Studies. Appendices: A: Notation, Symbols, and Constants. B: Matrix Review.
906 citations
TL;DR: A vision-based mobile robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments to localize itself accurately and build a map of the environment.
Abstract: A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser rang...
904 citations
03 Nov 1991
TL;DR: Discusses a significant open problem in mobile robotics: simultaneous map building and localization, which the authors define as long-term globally referenced position estimation without a priori information.
Abstract: Discusses a significant open problem in mobile robotics: simultaneous map building and localization, which the authors define as long-term globally referenced position estimation without a priori information. This problem is difficult because of the following paradox: to move precisely, a mobile robot must have an accurate environment map; however, to build an accurate map, the mobile robot's sensing locations must be known precisely. In this way, simultaneous map building and localization can be seen to present a question of 'which came first, the chicken or the egg?' (The map or the motion?) When using ultrasonic sensing, to overcome this issue the authors equip the vehicle with multiple servo-mounted sonar sensors, to provide a means in which a subset of environment features can be precisely learned from the robot's initial location and subsequently tracked to provide precise positioning. >
898 citations
01 Sep 1989
TL;DR: The issues involved in integrating multiple sensors into the operation of a system are presented in the context of the type of information these sensors can uniquely provide, along with proposed high-level multisensory representations suitable for mobile robot navigation and control.
Abstract: The issues involved in integrating multiple sensors into the operation of a system are presented in the context of the type of information these sensors can uniquely provide. A survey is provided of the variety of approaches to the problem of multisensor integration and fusion that have appeared in the literature in recent years ranging from general paradigms, frameworks, and methods for integrating and fusing multisensory information to existing multisensor systems used in different areas of application. General multisensor fusion methods, sensor selection strategies, and world models are examined, along with approaches to the integration and fusion of information from combinations of different types of sensors. Short descriptions of the role of multisensor integration and fusion in the operation of a number of existing mobile robots are provided, together with proposed high-level multisensory representations suitable for mobile robot navigation and control. Existing multisensor systems for industrial and other applications are considered. >
800 citations
TL;DR: This paper uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion, to demonstrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.
Abstract: This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.
789 citations