A method and system for object detection in lidar point clouds

By combining angle and distance constraints, interference point clouds are filtered out and missing point clouds are compensated, which solves the problems of insufficient object recognition accuracy and failure of continuous object segmentation of LiDAR under sparse point cloud data, and realizes fast and accurate object detection.

CN117011271BActive Publication Date: 2026-05-01SHANDONG UNIV
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Patent Information

Application Number
CN202310982807.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-08-04
Publication Date
2026-05-01
Estimated Expiration
2043-08-04

AI Technical Summary

Technical Problem

Existing LiDAR systems suffer from insufficient object recognition accuracy and high cost under sparse point cloud data, and single-angle constraints lead to failure in edge segmentation of continuous large-area objects.

Method used

A method combining angle and distance constraints is adopted to generate a two-dimensional distance image by filtering out interfering point clouds and compensating for missing point clouds. The point cloud is then segmented according to angle and distance constraints to avoid segmentation failure from a single angle.

Benefits of technology

Achieve fast and accurate object detection on sparse point cloud data, meet real-time requirements, and improve clustering accuracy and detail preservation.

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Abstract

The application discloses a laser radar point cloud object detection method and system, comprising the following steps: obtaining a three-dimensional point cloud of a laser radar, and after filtering interference point clouds and compensating for missing point clouds, obtaining a to-be-segmented point cloud; generating a two-dimensional distance image from the to-be-segmented point cloud; setting an angle constraint; the angle constraint is that the included angle between the line connecting two adjacent points in the two-dimensional distance image and the line connecting the radar center and the point farther away from the radar center among the two adjacent points is not less than an angle threshold; setting a distance constraint; the distance constraint is that the distance between the two adjacent points in the two-dimensional distance image is not greater than a distance threshold; traversing the points in the two-dimensional distance image, judging whether the two adjacent points satisfy the angle constraint or the distance constraint, if any constraint is satisfied, marking the two adjacent points as the same object, otherwise, marking the two adjacent points as different objects, and thus obtaining an object segmentation result. The object can be quickly detected under sparse point cloud data.
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