Gaussian distribution based mobile robot simultaneous localization and mapping method

Abstract

The invention relates to a Gaussian distribution resampling Rao-Blackwellized particle filter based mobile robot simultaneous localization and mapping method. The method comprises the following steps: S1, robot initial pose is estimated according to robot pose and mileometer control information; S2, a scan matching method is executed according to a map; S3, particle sampling is carried out in proposal distribution of trajectory; S4, weight of each particle is calculated and weight of each particle is updated; S5, particle resampling is carried out on the basis of Gaussian distribution: specifically, by sorting particle weight, high-weight particles are dispersed to obtain resampled new particles; and S6, the map is calculated according to robot pose and observation information, and map revision is carried out. By the method, reliable grid map precision can be obtained.

Description

technical field [0001] The invention belongs to the field of mobile robot navigation, in particular to a Gaussian distribution simultaneous positioning and map construction method Background technique [0002] Simultaneous Localization and Mapping (SLAM) of mobile robots is a mobile robot that creates a map based on its own pose estimation and sensor observation data in a completely unknown environment under the condition that its own position is uncertain. SLAM was first proposed by Smith, Self and Cheeseman, which solves the problem of obtaining a series of observations from a mobile robot to construct an unknown key map. [0003] In the study of SLAM problems, for the state estimation of nonlinear and non-Gaussian systems, the current research hotspot is the particle filter algorithm based on the Sequential Monte Carlo method (Sequential Monte Carlo, SMC). The algorithm has no limitation on the system noise, and it can approximate the recursive Bayesian estimation of the...

AI Technical Summary

Problems solved by technology

This improves particle degradation, but at the same time causes particle starvation

For this reason, the method of adaptive resampling has become the focus of research. Moral et al. conducted a detailed analysis of the adaptive resampling strategy and convergence. Some adaptive improved resampling SLAM algorithms were proposed, although these algorithms can suppress the lack of samples. phenomenon, improve the estimation accuracy of the particle filter algorithm, but the cost of the algorithm is still to increase the calculation time, although the algorithm can be implemented with a small number of particles, it can only achieve a limited balance between accuracy and time

Images