Positioning and perception in LIDAR point clouds

被引:17
|
作者
Benedek, Csaba [1 ]
Majdik, Andras [1 ]
Nagy, Balazs [1 ]
Rozsa, Zoltan [1 ]
Sziranyi, Tamas [1 ]
机构
[1] Eotvos Lorand Res Network ELKH, Inst Comp Sci & Control SZTAKI, Machine Percept Res Lab MPLab, Kende U 13-17, H-1111 Budapest, Hungary
关键词
Lidar; Object detection; SLAM; Change detection; Navigation; OBJECT DETECTION; PEOPLE; VEHICLES; FEATURES;
D O I
10.1016/j.dsp.2021.103193
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the last decade, Light Detection and Ranging (LIDAR) became a leading technology of detailed and reliable 3D environment perception. This paper gives an overview of the wide applicability of LIDAR sensors from the perspective of signal processing for autonomous driving, including dynamic and static scene analysis, mapping, situation awareness which functions significantly point beyond the role of a safe obstacle detector, which was the sole typical function for LIDARs in the pioneer years of driver-less vehicles. The paper focuses on a wide range of LIDAR data analysis applications of the last decade, and in addition to the presentation of a state-of-the-art survey, the article also summarizes some issues and expected directions of the development in this field, and the future perspectives of LIDAR systems and intelligent LIDAR based information processing. (C) 2021 The Author(s). Published by Elsevier Inc.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] FOG: Fast Octree Generator for LiDAR Point Clouds
    Roriz, Ricardo
    Costa, Diogo
    Ekpanyapong, Mongkol
    Gomes, Tiago
    IEEE Sensors Letters, 2025, 9 (01):
  • [42] Object Segmentation of Cluttered Airborne LiDAR Point Clouds
    Caros, Mariona
    Just, Ariadna
    Segui, Santi
    Vitria, Jordi
    ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT, 2022, 356 : 259 - 268
  • [43] IMPROVED TORNADO METHOD FOR GROUND POINT FILTERING FROM LIDAR POINT CLOUDS
    Mahphood, A.
    Arefi, H.
    ISPRS GEOSPATIAL CONFERENCE 2022, JOINT 6TH SENSORS AND MODELS IN PHOTOGRAMMETRY AND REMOTE SENSING, SMPR/4TH GEOSPATIAL INFORMATION RESEARCH, GIRESEARCH CONFERENCES, VOL. 10-4, 2023, : 429 - 436
  • [44] NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields
    Zhang, Junge
    Zhang, Feihu
    Kuang, Shaochen
    Zhang, Li
    THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 7, 2024, : 7178 - 7186
  • [45] 3D Point Clouds Data Super Resolution-Aided LiDAR Odometry for Vehicular Positioning in Urban Canyons
    Yue, Jiang
    Wen, Weisong
    Han, Jin
    Hsu, Li-Ta
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (05) : 4098 - 4112
  • [46] Classification of LiDAR Point Clouds Using Supervoxel-Based Detrended Feature and Perception-Weighted Graphical Model
    Xu, Yusheng
    Ye, Zhen
    Yao, Wei
    Huang, Rong
    Tong, Xiaohua
    Hoegner, Ludwig
    Stilla, Uwe
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2020, 13 : 72 - 88
  • [47] A segmentation method for LiDAR point clouds of aerial slender targets
    Huang, Birong
    Wang, Zilong
    Chen, Jianhua
    Zhou, Bingyang
    Ma, Hao
    Frontiers in Physics, 2025, 13
  • [48] A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds
    Roriz, Ricardo
    Silva, Heitor
    Dias, Francisco
    Gomes, Tiago
    SENSORS, 2024, 24 (10)
  • [49] A Fast Edge Extraction Method for Mobile Lidar Point Clouds
    Xia, Shaobo
    Wang, Ruisheng
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 14 (08) : 1288 - 1292
  • [50] Analysis of Airborne LiDAR Point Clouds With Spectral Graph Filtering
    Bayram, Eda
    Frossard, Pascal
    Vural, Elif
    Alatan, Aydin
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2018, 15 (08) : 1284 - 1288