Continuous probabilistic mapping by autonomous robots

Abstract

Abstract. In this paper, we present a new approach for continuous probabilistic mapping. The objective is to build metric maps of unknown environments through cooperation between multiple autonomous mobile robots. The approach is based on a B a yesian update rule that can be used to integrate the range sensing data coming from multiple sensors on multiple robots. In addition, the algorithm is fast and computationally inexpensive so that it can be implemented on small robots with limited computation resources. The paper describes the algorithm and illustrates it with experiments in simulation and on real robots.

Modeling Multi-Robot Terrain Mapping Using Hybrid Dynamics

“…The issues of robot mapping have been studied by many researchers. Thrun et al [2][3][4], Tercero et al [5], Huber and Hebert [6], Kim and Oh [7], and Pascual et al [8] have proposed mapping algorithms for concurrent mapping and localization. Some coordination mechanisms have been adopted by Burgard et al [9] and Rekleitis et al [10].…”