On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Topological localization with kidnap recovery using sonar grid map matching in a home environment
Robotics and Computer-Integrated Manufacturing
Hi-index | 0.00 |
Simultaneous localization and mapping (SLAM) is a well-studied problem in mobile robotics. However, the majority of the proposed techniques for SLAM rely on the use of accurate and dense measurements provided by laser rangefinders to correctly localize the robot and produce accurate and detailed maps of complex environments. Little work has been done on the use of low-cost but noisy and sparse sonar sensors for SLAM in large indoor environments involving large loops. In this paper, we present our approach to SLAM with sonar sensors by applying particle filtering and a line-segment-based map representation with an orthogonality assumption to map indoor environments much larger and more challenging than those previously considered with sonar sensors. Results from robotic experiments demonstrate that it is possible to produce good maps of large indoor environments with large loops despite the inherent limitations of sonar sensors.