An improved Monte Carlo localization using optimized iterative closest point for mobile robots

This paper details a solution of fusing combination features, Iterative Closest Point (ICP) and Monte Carlo algorithm, in order to solve the problem that mobile robot positioning is easy to fail in a dynamic environment. Firstly, an ICP algorithm based on the maximum common combination feature is proposed to provide a more stable observation point information and therefore avoids the problem of local extremes and obtains more accurate matching results. A novel proposal distribution is then designed and auxiliary particles are used, so that the particle sets are distributed in high-observational areas closer to the true posterior probability of the state. Finally, the experimental results on the public datasets show that the proposed algorithm is more accurate in these environments.