Laser mapping method and device based on hierarchical switchable sparse pose map optimization

Abstract

The invention provides a laser mapping method and device based on hierarchical switchable sparse pose map optimization. The method comprises the following steps: acquiring laser radar multi-frame scanning data; modeling the pose of the robot by using a continuous time system, and tracking the pose of the robot through laser matching; constructing local sub-graphs, calculating construction inter-frame motion constraints and closed-loop constraints among the local sub-graphs, and constructing a pose graph; and performing hierarchical pose map optimization according to the type of the inter-frame constraint, updating the position of each sub-map, and updating the environment map. According to the method, the efficiency, the precision and the anti-interference performance in the mapping process are improved by using pose map optimization, so that the adaptability of the algorithm to a low-performance computing resource plate can be improved, and the algorithm provides a more accurate environment model for motion planning control of autonomous navigation; and accurate positioning and motion planning control in the environment are facilitated, so that the intelligent degree of the autonomous navigation robot is improved.

Description

technical field [0001] The invention belongs to the technical field of autonomous navigation and positioning, and in particular relates to a laser mapping method and device based on hierarchical switchable sparse pose graph optimization. Background technique [0002] Among the 2D laser mapping methods, the method based on pose graph optimization has gradually become the mainstream. The more famous open source projects include Cartographer, OpenKarto, and so on. This is mainly due to the fact that the method can reduce the errors accumulated by the robot in the process of large-scale mapping through closed-loop detection and pose graph optimization, and then obtain better quality mapping results. However, in the actual application process, often because of the introduction of wrong motion constraints (False Positive Constraints) in the process of constructing the pose graph, the result is optimization errors, and the overall pose graph is messed up, resulting in the failure o...

AI Technical Summary

Problems solved by technology

However, in practical applications, such estimates are often discrete, resulting in situations where it may not be possible to fully ensure the effective alignment and matching of radar data and maps

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