Abstract
In this paper, we propose a localization method integrating laser range finder (LRF) measurement and odometry by moving horizon estimation (MHE). The position and the attitude angle of a robot are estimated by solving an optimization problem using multiple sampling data while shifting the evaluation interval at each sampling. The relative importance of the estimations of LRF and odometry are balanced by adapting to the number of LRF measurements. With the proposed method, accurate localization can be realized even in singular environments where the self-pose cannot be localized using only LRF measurements. The accuracy of the proposed method is verified through comparisons with conventional methods on numerical simulations. In addition, we verify the robustness and feasibility of the proposed method for singular environments through numerical simulations and experiments.
