Robot anti-interference positioning method and device based on sliding window pose map optimization

Abstract

The invention provides a robot anti-interference positioning method based on sliding window pose map optimization, and the method comprises the steps: obtaining laser data, and carrying out the repositioning of a robot: judging a search range according to whether the robot has an initial pose, and determining the current pose of the robot; tracking real-time pose during robot movement: judging whether enough key frames are accumulated in the tracking process or not, if yes, deleting the earliest key frame, and storing the latest key frame; judging the current positioning state of the robot , if the current positioning is lost, obtaining the latest accurate positioning again, updating the positioning , and continuing pose tracking with the latest pose; otherwise, performing inner point screening and accumulated error elimination so as to allow the robot to continuously output poses in the map to achieve real-time accurate positioning . The method has the beneficial effects that global repositioning can be realized without long-time movement, and the anti-interference capability is improved.

Description

technical field [0001] The invention belongs to the technical field of autonomous navigation and positioning, and in particular relates to a robot anti-jamming positioning method and device based on sliding window pose graph optimization. Background technique [0002] The precise positioning of the robot is a necessary prerequisite for the robot to complete autonomous navigation. In the existing technology, the pose of the robot is often judged indirectly by installing markers such as magnetic nails and magnetic strip reflectors. However, this method requires modification of the existing environment and is not suitable for most scenarios. In addition, laser SLAM (simultaneous localization and mapping) is used to construct a priori map, and based on the existing prior map, the adaptive Monte Carlo algorithm (adaptive Monte CarloLocalization; AMCL) is used for robot positioning. Larger and the output estimation results are not unique, global relocation is difficult without a ...

AI Technical Summary

Problems solved by technology

In the existing technology, the pose of the robot is often judged indirectly by installing markers such as magnetic nails and magnetic strip reflectors. However, this method requires modification of the existing environment and is not suitable for most scenarios.

In addition, laser SLAM (simultaneous localization and mapping) is used to construct a priori map, and based on the existing prior map, the adaptive Monte Carlo algorithm (adaptive Monte Carlo Localization; AMCL) is used for robot positioning. Larger and the output estimation results are not unique, global relocation is difficult without a priori pose, or the robot needs to move for a long time. The actual use effect is not good, and in some areas where changes occur, the positioning accuracy Variation, using the original cartographer pose graph optimization method for robot positioning, the calculation is large, not suitable for low-cost robots

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