Continuous Probabilistic Mapping by Autonomous Robots

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 Bayesian 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.

• Type: Conference
• Year: 1999
• Where: ISER
• Authors: Jesús Salido Tercero, Christiaan J. J. Paredis, Pradeep K. Khosla
• Keywords: Autonomous Mobile Robots, Continuous Probabilistic Mapping, ISER 1999, Multiple Robots, Robotics