Monte Carlo localization: efficient position estimation for mobile robots

摘要: This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is version of Markov family probabilistic approaches that have recently been applied with great practical success. However, previous were either computationally cumbersome (such as grid-based represent the state space by high-resolution 3D grids), or had to resort extremely coarse-grained resolutions. Our approach efficient while retaining ability (almost) arbitrary distributions. applies sampling-based methods approximating probability distributions, in way places computation "where needed." The number samples adapted on-line, thereby invoking large sample sets only when necessary. Empirical results illustrate yields improved accuracy requiring an order magnitude less compared approaches. It also much easier implement.