Efficient Multi-Robot
Localization Based on Monte Carlo Approximation
Proc. of the 9th International Symposium of
Robotics Research (ISRR-99), 1999
Abstract
This paper presents a probabilistic algorithm for
collaborative mobile robot localization. Our approach uses a
sample-based version of Markov localization, capable of localizing
mobile robots in an any-time fashion. When teams of robots localize
themselves in the same environment, probabilistic methods are
employed to synchronize each robot's belief whenever one robot
detects another. As a result, the robots localize themselves faster,
maintain higher accuracy, and high-cost sensors are amortized across
multiple robot platforms. The paper also describes experimental
results obtained using two mobile robots, using computer vision and
laser range-finding for detecting each other and estimating each
other's relative location. The results, obtained in an indoor office
environment, illustrate drastic improvements in localization speed
and accuracy when compared to conventional single-robot
localization.