Map construction method for accurate positioning based on multi-segment joint optimization

Abstract

The invention discloses a map construction method for accurate positioning based on multi-section joint optimization, and belongs to the field of accurate positioning of robots or automatic driving automobiles based on prior maps. The method comprises the following steps: (1) for a scene needing to construct a positioning map, using a feature point-based visual SLAM (Simultaneous Localization and Mapping) method to obtain a plurality of groups of single-segment sub-maps through a visual odometer and local map optimization, and carrying out parallel operation on the visual odometer and the local map optimization in two independent threads; (2) utilizing ORB descriptors in the key frames, and adopting a scene recognition strategy based on a bag-of-words model to carry out rapid overlap detection between the sub-maps; (3) executing multi-section pose map optimization in a global coordinate system by utilizing the anchor points distributed to each sub-map; and (4) combining all the sub-maps into an integral map, and then performing global BA optimization on the integral map so as to obtain a more accurate offline map capable of being used for accurate positioning.

Description

technical field [0001] In the present invention, a map construction method for precise positioning based on multi-stage joint optimization is designed. This method only uses an inexpensive binocular camera as the input sensor and consists of two key modules. The first module is the binocular visual SLAM front end, which uses the feature point-based visual SLAM (Simultaneous Localization and Mapping, simultaneous positioning and map construction) method to obtain multiple single-segment sub-maps for the same scene. The second key module is the multi-segment merging backend, in which multiple single-segment submaps are jointly optimized to obtain a more accurate map. This method can be applied to the field of precise positioning of robots or self-driving cars based on prior maps, and low-cost, low-precision camera sensors can be used to obtain prior maps for precise positioning. Background technique [0002] With the continuous development of mobile robots and self-driving c...

AI Technical Summary

Problems solved by technology

However, the millimeter-wave radar used in this method is still more expensive than the camera sensor, and the noise-filled radar data makes feature extraction more complicated, thereby affecting accuracy

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