الصفحة 1
الصفحة 1
FastSLAM : A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics
This monograph describes a new family of algorithms for the simultaneous localization and mapping problem in robotics (SLAM). SLAM addresses the problem of acquiring an environment map with a roving robot, while simultaneously localizing the robot relative to this map. This problem has received enormous attention in the robotics community in the past few years, reaching a peak of popularity on the occasion of the DARPA Grand Challenge in October 2005, which was won by the team headed by the authors. The FastSLAM family of algorithms applies particle filters to the SLAM Problem, which provides new insights into the data association problem that is paramount in SLAM. The FastSLAM-type algorithms have enabled robots to acquire maps of unprecedented size and accuracy, in a number of robot application domains and have been successfully applied in different dynamic environments, including the solution to the problem of people tracking.
DARPA Grand Challenge : The Great Robot Race
The goal of the new series of Springer Tracts in Advanced Robotics (STAR) is to bring, in a timely fashion, the latest advances and developments in robotics on the basis of their significance and quality. It is our hope that the wider dissemination of research developments will stimulate more exchanges and collaborations among the research community and contribute to further advancement of this rapidly growing field. The volume edited by Martin Buehler, Karl Iagnemma and Sanjiv Singh presents a unique and extensive collection of the scientific results by the teams which took part into the DARPA Grand Challenge in October 2005 in the Nevada desert. This event reached an incredible peak of popularity in the media, the race of the century like someone called it! The Grand Challenge demonstrated the fast growing progress - ward the development of robotics technology, as it showed the feasibility of using mobile robots operating autonomously in real world scenarios.
