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

Abstract

Abstract 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.

Keywords

• auxiliary particles
• combined features
• ICP algorithm
• Monte Carlo localization
• proposal distribution