A Localizability Estimation Method for Mobile Robots Based on 3D Point Cloud Feature

This paper introduces the theoretical analysis and derivation of localizability estimation for mobile robots based on point cloud observation. Combined with the observation model, the pre-established point cloud map is firstly clustered and segmented with a newly proposed method considering multiple constraints on depth map. Then localizability is set to be equal to the strength of the constraints associated with 3D point cloud features. In addition, based on the method of using information matrix theory, this paper integrates the Fisher's information matrix and point cloud features to estimate localizability.