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兵工学报 ›› 2025, Vol. 46 ›› Issue (4): 240090-.doi: 10.12382/bgxb.2024.0090

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基于地面特征的越野环境轻量化回环检测

张森杰1,2, 龚建伟1, 齐建永1, 臧政1,3, 胡秀中1, 龚小杰1, 熊光明1,*()   

  1. 1 北京理工大学 机械与车辆学院, 北京 100081
    2 北京理工大学 长三角研究院, 浙江 嘉兴 314019
    3 北京理工大学 前沿技术研究院, 山东 济南 250300
  • 收稿日期:2024-01-30 上线日期:2025-04-30
  • 通讯作者:
  • 基金资助:
    国家自然科学基金项目(52372404)

Lightweight Loop Closure Detection of Off-road Environment Based on Ground Features

ZHANG Senjie1,2, GONG Jianwei1, QI Jianyong1, ZANG Zheng1,3, HU Xiuzhong1, GONG Xiaojie1, XIONG Guangming1,*()   

  1. 1 School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    2 Yangtze Delta Region Academy, Beijing Institute of Technology, Jiaxing 314019, Zhejiang, China
    3 Institute of Advanced Technology, Beijing Institute of Technology, Jinan 250300, Shandong, China
  • Received:2024-01-30 Online:2025-04-30

摘要:

为解决传统回环检测算法依赖里程计精度与外部定位信息、消耗过多计算资源以及现有轻量化回环检测算法平移不变性差、难以适应越野场景下稀疏环境特征等问题,提升无人驾驶平台在卫星拒止条件下执行长时间、大范围行进任务的定位能力,提出一种利用激光雷达点云对地面特征进行描述的轻量化回环检测算法。不同于现有的利用深度学习从单帧或多帧点云中提取点云特征并构建全局描述子,所提算法使用快速的激光点云地面特征描述方式,实现单帧点云快速特征提取与全局一致的位置特征描述,将多帧激光点云地面特征聚合为子地图回环检测描述子;在临近帧之间依靠里程计位姿实现轻量化全局描述子的构建,不依赖先验位置信息进行全局描述子的匹配并实现回环检测;在越野环境下以机械式激光雷达、固态激光雷达对算法进行实车验证。研究结果表明,与已有的轻量化回环检测算法对比,验证了该算法在越野环境下回环检测高召回率、实时性好、占用资源少的优势。

关键词: 越野环境, 激光雷达, 地面特征, 回环检测

Abstract:

The traditional loop closure detection algorithm relies on the accuracy of odometer and the external global positioning information,which consumes too much computing resources,and the existing lightweight loop closure detection algorithm has poor translation invariance and difficulty in adapting to the sparse environmental characteristics in off-road road environment.In order to improve the positioning capability of unmanned platform in the condition of satellite rejection for a long time and a large range of tasks,a lightweight loop closure detection algorithm using light detection and ranging (LiDAR) point clouds to describe the ground feature is proposed.It is different from extracting the point cloud features from single or multi-frame point clouds by deep learning.And a global descriptor is constructed.The fast LiDAR point clouds ground feature description approach is used to achieve the fast feature extraction of single frame point cloud and the globally consistent position feature description,and the multi-frame LiDAR point clouds ground features are aggregated into the sub-map loop closure detection descriptors.A lightweight global descriptor is constructed by odometer pose between adjacent frames,and the global descriptors are matched and the loop closure detection is realized without prior position information.The proposed algorithm is verified by using the mechanical and solid-state LiDAR in off-road environment.Compared with the existing lightweight loop closure detection algorithms,the proposed algorithm has the advantages of high recall rate,good real-time performance and less resource consumption in the off-road environment.

Key words: off-road environment, light detection and ranging, ground feature, loop closure detection