AMCL positioning method adaptive to dynamic environment change

Abstract

The invention discloses an AMCL positioning method adapted to dynamic environment change, which comprises the following steps: (1) enabling a robot to move to obtain observation data, and projecting the observation data to a map coordinate system according to the pose of a robot coordinate system in the map coordinate system; (2) calculating the matching degree between the observation data and the off-line map, and comparing the matching degree with a set threshold value; (3) taking the robot coordinate system poses corresponding to the observation data as nodes, constructing a pose graph through constraint, taking the nodes corresponding to the observation data with the matching degree higher than a set threshold value as anchor points, and taking the nodes corresponding to the observation data with the matching degree lower than the set threshold value as optimization items; (4) obtaining an optimized robot coordinate system pose, and obtaining an updated offline map; and (5) enabling the robot to carry out positioning by using the updated offline map. According to the method, the situation that original AMCL only depends on an off-line map for positioning is improved, the ability of an AMCL positioning algorithm to adapt to a dynamic scene can be improved, and the stability of robot positioning is improved.

Description

technical field [0001] The invention relates to the field of positioning, in particular to an AMCL positioning method adapting to dynamic environment changes. Background technique [0002] The AMCL algorithm is a filter-based positioning algorithm, which mainly matches the real-time data of the ranging sensor with the loaded offline map to calculate the accurate pose of the robot in the map. AMCL positioning algorithms are widely used in industries such as unmanned driving and robotics. [0003] Monte Carlo localization (MCL) is based on the particle filter algorithm. One of its major advantages is that it is not limited by the scene, the algorithm is simple and fast, and it can also take into account the accuracy of the algorithm. Particle filtering uses particle density (that is, the number of particles in a unit interval) to characterize the probability of occurrence of an event. Infers the confidence level of the event according to the selected evaluation equation, and...

AI Technical Summary

Problems solved by technology

[0004] The disadvantage of the current AMCL positioning algorithm is that it loads offline map files

In practical applications, the environment is often changing, such as the ever-changing flow of people in the shopping mall, the vehicles parked on the roadside in the outdoor environment, etc., the stability of the AMCL positioning algorithm is poor in the dynamic environment

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