A Location Recognition and Positioning Method for Autonomous Unmanned Systems Based on Sequence Image Features

Abstract

The invention discloses a location recognition and positioning method for an autonomous unmanned system based on sequence image features. Firstly, the image features to be extracted are extracted through an improved convolutional neural network model, and the obtained depth features have strong illumination invariance and viewing angle invariance. The robustness of the algorithm to changes in scene conditions and changes in the robot's perspective is enhanced; then the difference measurement method based on image sequences is used to effectively provide constraints for the position recognition of adjacent frames and improve the accuracy of recognition; secondly, the approximate nearest neighbor The search method greatly reduces the calculation amount of sequence search and improves the efficiency of use in large-scale environments; finally, through the method of dynamically updating candidate matching, it effectively reduces the omissions caused by sequence search and improves the efficiency of the algorithm. Tolerance rate. The visual position recognition algorithm of the present invention has outstanding advantages such as strong robustness, high efficiency, adaptability to various scenes, and the like.

Description

technical field [0001] The invention belongs to the field of mobile robots, and relates to a position recognition and positioning method of an autonomous unmanned system based on sequence image features. Background technique [0002] Realizing long-term autonomous navigation and positioning of robots in a dynamically changing environment is one of the main research difficulties and hotspots in mobile robot technology. How to perform efficient position recognition in long-term and large-scale motion environments has become an urgent need to solve The problem. Vision-based position recognition technology retrieves and matches the current image acquired by the robot with the reference image in the map to determine the current position of the robot in the map. When a robot moves for a long time in a large-scale scene, it is in a dynamically changing environment. Affected by factors such as illumination, seasons, weather, occluders, moving objects, and shooting angles, the appea...

AI Technical Summary

Problems solved by technology

However, CNN-based image feature descriptors also have a high dimensionality, which tends to cause a large amount of calculation when performing similarity measurement. Usually, certain dimensionality reduction and optimization are performed on it before subsequent operations.

In addition, the map obtained by moving in a large-scale scene will be relatively large, and it will consume a lot of time when performing retrieval tasks

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