Enhanced radar inertial positioning method and system based on visual descriptor

By using visual descriptors to assist in eliminating false matches and adaptive covariance modeling, the defects of radar inertial positioning methods in false match elimination and adaptive covariance modeling are solved, and the positioning accuracy and robustness are improved.

CN120778100APending Publication Date: 2025-10-14SOUTHEAST UNIV
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Patent Information

Application Number
CN202510897659.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-01
Publication Date
2025-10-14

AI Technical Summary

Technical Problem

Existing radar-inertial positioning methods have defects in false matching elimination and matching adaptive covariance modeling, resulting in insufficient compatibility and poor robustness of positioning methods in scenarios with inconsistent scales.

Method used

An enhanced radar-inertial positioning method based on visual descriptors is adopted. By preprocessing and aligning the timestamps of each frame of lidar point cloud, features are extracted and visual descriptors are constructed. False matches are eliminated, and point-line and point-surface adaptive covariance models are established to finally perform pose estimation.

Benefits of technology

The radar point cloud mismatch rejection rate is improved, the robustness of the positioning method in complex environments is enhanced, and the positioning accuracy is improved.

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Abstract

The invention discloses an enhanced radar inertial positioning method and system based on a visual descriptor, and the method comprises the steps: carrying out the preprocessing of a collected laser radar point cloud, and aligning each frame of image to a timestamp of a corresponding point cloud frame; carrying out feature extraction on the laser radar point cloud, projecting each feature to a picture plane to complete descriptor construction, carrying out sampling on a matching line and a plane, carrying out feature matching elimination according to a Hamming distance between descriptors of matched features, and completing visual descriptor auxiliary elimination; modeling is carried out on point features according to the descriptor distance, modeling is carried out on a matching line and a matching surface, and finally point-line and point-surface matching adaptive covariance modeling is completed; and finally, executing pre-integration by using the inertial data, performing scanning matching between the point clouds according to the adaptive covariance, and inputting a pre-integration result and a scanning matching result into a pose map to obtain pose estimation and complete positioning. According to the method, mismatching in point cloud features can be accurately eliminated, and the positioning precision is improved.
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