利用激光点云测距影像的城市环境动态目标检测优化方法

doi:10.13474/j.cnki.11-2246.2025.0207

测绘通报 ›› 2025, Vol. 0 ›› Issue (2): 35-40.doi: 10.13474/j.cnki.11-2246.2025.0207

利用激光点云测距影像的城市环境动态目标检测优化方法

乌萌^1,2, 熊超^1,2

1. 地理信息工程国家重点实验室, 陕西西安 710054;
2. 西安测绘研究所, 陕西西安 710054

收稿日期:2024-06-21 发布日期:2025-03-03
作者简介:乌萌(1983—),女,博士,高级工程师,研究方向为地面无人平台视觉导航与环境地图构建。E-mail:wumeng19nudt@163.com
基金资助:
地理信息工程国家重点实验室自主研究项目(SKLGIE2022-ZZ2-03);2022国家社科基金项目(2022-SKJJ-B-063)

An optimized dynamic object detection method using LiDAR point cloud range images for urban environment

WU Meng^1,2, XIONG Chao^1,2

1. State Key Laboratory of Geo-information Engineering, Xi'an 710054, China;
2. Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China

Received:2024-06-21 Published:2025-03-03

摘要/Abstract

摘要： 针对利用激光雷达测距影像开展动态目标检测中存在的区域过度生长问题,本文提出了一种城市环境动态目标检测优化方法。在二维测距影像中进行三维点云空间快速分割旋转,准确、高效地保证了不依赖目标模型情况下的目标正确区域生长;同时针对场景流检测动态目标作了分类特性分析,通过目标聚类和目标区域生长解决了检测场景流后动态目标漏检测、超长目标误检测等问题,在显著提升检测查准率和查全率的同时,保证了较好的计算效率。相比直接在三维点云进行动态目标检测的算法,本文算法的帧平均计算时间是其1/11,计算查准率提升了12.67%,查全率提升了1.51%。

关键词: 无人驾驶车, 激光点云, 测距影像, 动态目标检测

Abstract: To address the problem of overgrowing regions in dynamic object detection using LiDAR range images, an optimized dynamic object detection method for urban environments is proposed in this paper. This method performs fast segmentation and rotation of 3D point cloud space in 2D range images, ensuring accurate and efficient growth of the correct object region without relying on the object model. Additionally, a classification characteristic analysis for dynamic objects in scene flow detection is conducted. The issues of missed detection of dynamic objects and false detection of excessively long objects are resolved through object clustering and object region growth. This significantly improves the detection precision and recall rate while maintaining good computational efficiency. Compared to algorithms that directly detect dynamic objects in 3D point clouds, this algorithm achieves an average frame calculation time of 1/11, with a 12.67% increase in precision and a 1.51% increase in recall rate.

Key words: autonomous vehicle, LiDAR point cloud, range image, dynamic object detection

中图分类号:

P228

乌萌, 熊超. 利用激光点云测距影像的城市环境动态目标检测优化方法[J]. 测绘通报, 2025, 0(2): 35-40.

WU Meng, XIONG Chao. An optimized dynamic object detection method using LiDAR point cloud range images for urban environment[J]. Bulletin of Surveying and Mapping, 2025, 0(2): 35-40.

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利用激光点云测距影像的城市环境动态目标检测优化方法

An optimized dynamic object detection method using LiDAR point cloud range images for urban environment

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