A Computationally Efficient Method for Large-Scale Concurrent Mapping and Localization
Decoupled stochastic mapping (DSM) is a computationally efficient approach to large-scale concurrent mapping and localization. DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constant-time algorithm whose memory requirements scale linearly with the size of the operating area. The performance of two different variations of the algorithm is demonstrated through simulations of environments with 110 and 1200 features. Experimental results are presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 meter testing tank.
KeywordsMobile Robot Error Bound Extended Kalman Filter Survey Area Autonomous Underwater Vehicle
Unable to display preview. Download preview PDF.
- R. A Brooks. Aspects of mobile robot visual map making. In Second Int. Symp. Robotics Research, Tokyo, Japan, 1984. MIT Press.Google Scholar
- R. Chatila and J.P. Laumond. Position referencing and consistent world modeling for mobile robots. In IEEE International Conference on Robotics and Automation. IEEE, 1985.Google Scholar
- M. W. M. G. Dissanayake, P. Newman, H. F. Durrant-Whyte, S. Clark, and M. Csorba. A solution to the simultaneous localization and map building (SLAM) problem. In Sixth International Symposium on Experimental Robotics, March 1999.Google Scholar
- R. Smith, M. Self, and P. Cheeseman. A stochastic map for uncertain spatial relationships. In 4th International Symposium on Robotics Research. MIT Press, 1987.Google Scholar
- P. Moutarlier and R. Chatila. Stochastic multisensory data fusion for mobile robot location and environment modeling. In 5th Int. Symposium on Robotics Research, Tokyo, 1989.Google Scholar
- J. K. Uhlmann, S. J. Julier and M. Csorba. Nondivergent Simultaneous Map Building and Localisation using Covariance Intersection. Navigation and Control Technologies for Unmanned Systems II, 1997.Google Scholar
- R. N. Carpenter. Concurrent mapping and localization using forward look sonar. In IEEE AUV, Cambridge, MA, August 1998.Google Scholar
- H. J. S. Feder, C. M. Smith, and J. J. Leonard. Incorporating environmental measurements in navigation. In IEEE AUV, Cambridge, MA, August 1998.Google Scholar
- J. J. Leonard and H. J. S. Feder. Decoupled stochastic mapping. Marine Robotics Laboratory Technical Report 99–1, Massachusetts Institute of Technology, 1999.Google Scholar