Autonomous navigation laser radar-camera rapid fusion target detection method and device, and storage medium

By employing dynamic perspective transformation and columnar feature representation methods, the problem of poor real-time performance in LiDAR-camera fusion target detection is solved, achieving more efficient target detection, reducing network complexity, and improving real-time performance.

CN121962569APending Publication Date: 2026-05-01STATE GRID JIANGSU ELECTRIC POWER CO LTD RESEARCH INSTITUTE +1
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Patent Information

Application Number
CN202511814129.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-04
Publication Date
2026-05-01

AI Technical Summary

Technical Problem

Existing LiDAR-camera fusion target detection schemes suffer from poor real-time target detection and complex neural network structures with high time complexity, which affects the real-time performance of environmental perception.

Method used

By employing a dynamic viewpoint transformer and a columnar feature representation method, the BEV features of LiDAR point clouds and camera images are acquired. Then, using a depth feature prediction module, a columnar feature representation module, a dynamic feature filtering module, and a grid dynamic projection module, the image features are converted into image BEV features and fused with the point cloud BEV features. This reduces the number of BEV operations and lowers the time and space complexity of the network structure.

Benefits of technology

It improves the real-time performance of target detection, reduces neural network inference time, and enhances the real-time performance of LiDAR-camera fusion perception.

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Abstract

The invention provides an autonomous navigation laser radar-camera fast fusion target detection method and device and a storage medium, and belongs to the field of multi-modal target detection, and the method comprises the steps: obtaining a laser radar point cloud and a camera image; obtaining point cloud BEV features according to the laser radar point cloud; performing feature extraction on the camera image to obtain a first image feature, and converting the first image feature into an image BEV feature by using a dynamic view angle converter; fusing the point cloud BEV features and the image BEV features to obtain fused BEV features; and a final target detection result is obtained. According to the invention, the operand of BEVPooling is reduced through the dynamic view angle converter, and the time and space complexity of the network structure is reduced, so that the reasoning time of the laser radar-camera fusion perception neural network is reduced, and the real-time performance of target detection is improved.
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