Efficient Multi-Robot Localization Based on Monte Carlo Approximation

摘要: This paper presents a probabilistic algorithm for collaborative mobile robot localization. Our approach uses sample-based version of Markov localization, capable localizing robots in any-time fashion. When teams localize themselves the same environment, methods are employed to synchronize each robot’s belief whenever one detects another. As result, faster, maintain higher accuracy, and high-cost sensors amortized across multiple platforms. The also describes experimental results obtained using two robots, computer vision laser range-finding detecting other estimating other’s relative location. results, an indoor office illustrate drastic improvements localization speed accuracy when compared conventional single-robot