沈阳工业大学学报2026，Vol.48Issue(2)：1-20,20.
基于LiDAR的目标检测算法与应用研究综述
Review of LiDAR-based object detection algorithms and applied research
摘要
Abstract
[Objective]Environmental perception is a core task in autonomous driving,and high-precision object detection is essential for ensuring the safety and stability of autonomous driving systems.In recent years,LiDAR has been widely adopted in autonomous driving as a key sensor for three-dimensional perception due to its advantages,such as immunity to lighting conditions and high ranging accuracy.[Methods]This study first reviewed traditional camera-based object detection methods and analyzed their limitations in complex environments,then introduced the development history,working principles,types,and key parameters of LiDAR,followed by a systematic review of object detection methods based on point cloud representations,voxel representations,and multi-sensor fusion strategies.The network architectures,advantages,and challenges of different methods were compared and analyzed,and quantitative performance evaluations were conducted based on experimental results from the KITTI detection dataset.In addition,this study introduced a perception framework based on the bird's eye view(BEV)perspective and the trend of multi-sensor fusion,and analyzed the trade-offs between detection accuracy,real-time performance,and environmental adaptability of the current algorithms.[Results]The advantages of LiDAR-based object detection were summarized,and key future research directions were proposed to address challenges such as point cloud sparsity,high computational overhead,and the complexity of multimodal fusion.[Conclusions]Continuous optimization of algorithms and hardware enhances the accuracy,robustness,and practicality of LiDAR object detection in complex scenes.关键词
激光雷达/目标检测/环境感知/点云处理/自动驾驶Key words
LiDAR/object detection/environmental perception/point cloud processing/autonomous driving分类
信息技术与安全科学引用本文复制引用
邓堡元,曹天翔,李育浩,彭子怡,廖怡晗,陈永灿,程亮,何赟泽..基于LiDAR的目标检测算法与应用研究综述[J].沈阳工业大学学报,2026,48(2):1-20,20.基金项目
广东省基础与应用基础研究基金省市联合基金重点项目(2023B1515120066) （2023B1515120066）
芙蓉计划科技领军人才项目(科技创新领军人才项目)(2023RC1039). （科技创新领军人才项目）
