改进的XGBoost用于LiDAR退化环境检测  

Improved XGBoost for LiDAR Degenerate Environment Detection

作　　者：徐爱功[1] 高佳鑫 隋心[1] 陈志键 王长强[1] 史政旭 XU Aigong;GAO Jiaxin;SUI Xin;CHEN Zhijian;WANG Changqiang;SHI Zhengxu(School of Geomatics,Liaoning Technical University,Fuxin 123000,China;Department of Military Oceanography and Hydrography and Cartography,Dalian Naval Academy,Dalian 116018,China)

机构地区：[1]辽宁工程技术大学测绘与地理科学学院,阜新123000 [2]大连舰艇学院军事海洋与测绘系,大连116018

出　　处：《地球信息科学学报》2025年第2期381-396,共16页Journal of Geo-information Science

基　　金：国家自然科学基金项目(42074012);辽宁省重点研发计划项目(2020JH2/10100044);辽宁省“兴辽英才计划”项目(XLYC2002101、XLYC2008034)。

摘　　要：【目的】目前,LiDAR退化环境检测方法存在需要启发式阈值、计算间接评价指标、检测效率低的问题。【方法】本文提出一种基于改进XGBoost的LiDAR退化环境检测方法。实现了从环境的几何结构角度直接检测单帧点云的退化情况。本文基于LiDAR点云数据构建分类特征体系,用于建立XGBoost决策树。在此基础上,采用模糊综合评价算法计算每个特征的综合重要性度量指标,用于构建有效的特征子集,从而提高检测精度。同时,通过一种基于Spearman秩相关系数的双向特征筛选策略来加速构建特征子集,从而提高模型的训练效率。针对XGBoost的初步检测结果,本文基于滑动窗口策略和多数投票策略对其进行二次修正,提高最终的LiDAR退化环境检测的精度。为验证本文方法的有效性以及对LiDAR退化环境的检测效果,通过搭建实验平台,采集真实场景数据并设计了相关实验。【结果】实验结果表明,本文方法各组成部分的有效性均能够被合理地验证;LiDAR退化环境检测成功率为94.41%,非退化误检测率为1.24%;相较于LOAM退化检测模块,检测成功率提高了10.91%,误检测率降低了95.26%,检测效率提高了56.97%。【结论】本文方法实现了高效率、高精度的LiDAR退化环境检测。[Objectives]Simultaneous Localization and Mapping(SLAM)using LiDAR technology is a pivotal technology in the fields of positioning and navigation and spatial data acquisition.It offers distinct advantages,such as reliable performance and the ability to generate intuitive maps.However,in environments lacking geometric features or with high feature similarity,traditional LiDAR SLAM techniques often yield inaccurate results,because the current LiDAR degenerate environment detection methods require heuristic thresholding,calculation of indirect evaluation indexes,and have low detection efficiency.In order to predict such anomalies and address this issue,it is crucial to effectively detect LiDAR degenerate environments.[Methods]This study proposed a LiDAR degenerate environment detection method based on improved XGBoost algorithms.It achieved direct degradation detection of individual-frame point clouds by analyzing the geometric structure of the environment.Specifically,a classification feature system was established utilizing LiDAR point cloud data,followed by the construction of XGBoost decision trees.Subsequently,the Fuzzy Comprehensive Evaluation(FCE)algorithm was employed to compute a comprehensive importance metric for each feature,thereby facilitating the formation of an effective subset of features which leads to enhanced detection accuracy.Meanwhile,a bidirectional feature screening strategy based on Spearman's rank Correlation Coefficient(SCC)was implemented to construct the feature subset and increase model training efficiency.Moreover,by integrating Sliding Window(SW)and Boyer-Moore Voting(BMV)strategies,we performed secondary correction on the initial XGBoost detection outcomes,further enhancing the precision of LiDAR degenerate environment detection.The validity of the proposed method and its effectiveness in detecting LiDAR degenerate environments were verified through the collection of real scene data.[Results]Our results demonstrated the efficacy of components of the method in this paper.The success

关 键 词：LiDAR退化环境 XGBoost 模糊综合评价 Spearman秩相关系数 双向特征筛选策略 滑动窗口策略 多数投票策略 LOAM 

分 类 号：TN958.98[电子电信—信号与信息处理] TP181[电子电信—信息与通信工程]