Review of three-dimensional environment information perception and reconstruction methods for mobile robot based on multi-sensor fusion

被引:1
|
作者
Liang Xiaoyue [1 ,2 ]
Bai Xu [1 ]
Zhang Songhai [2 ]
机构
[1] Beijing Peony Elect Grp Co Ltd, Postdoctoral Res Stn, Beijing 100191, Peoples R China
[2] Tsinghua Univ, Postdoctoral Res Stn, Comp Sci & Technol Dept, Beijing 100191, Peoples R China
关键词
mobile robots; multi-sensor fusion; information perception and reconstruction; mapping;
D O I
10.1117/12.2565371
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The technique of three-dimensional environment information perception and reconstruction is a model in which a subject equipped with a specific sensors establishes an environment and simultaneously estimates its own motion during movement without environmental prior information. This technique is widely used in application platforms such as driverless, unmanned aerial vehicle, and indoor robots. The research on mobile indoor robots has attracted wide attention as its diversity of application scenarios. In recent years, with the increasing maturity of sensor technology, a variety of sensors have been used in mobile robots to implement the function. Multi-sensor fusion to improve the quality of environment information perception and reconstruction has become the focus of research in this field. In this review, we discussed on the sensor types and hardware characteristics of multi-sensor fusion, and put forward that using cameras as the sensor core is the most widely used and best effect method. Furthermore, based on the research results in this field, multi-sensor fusion methods are divided into three levels: sensor data level, feature level and decision level. The implementation characteristics and effects of various methods based on typical technologies and representative papers are also discussed. Finally, the key issues to be solved in the multi-sensor fusion process are proposed, which points out the direction for the follow-up research work.
引用
收藏
页数:9
相关论文
共 50 条
  • [41] Multi-sensor Fusion and Cooperative Perception for Autonomous Driving A Review
    Xiang, Chao
    Feng, Chen
    Xie, Xiaopo
    Shi, Botian
    Lu, Hao
    Lv, Yisheng
    Yang, Mingchuan
    Niu, Zhendong
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2023, 15 (05) : 36 - 58
  • [42] Robot obstacle avoidance and navigation control research based on multi-sensor information fusion
    Fan, Xiaojing
    Jiang, Mingyang
    Pei, Zhili
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2018, 124 : 51 - 52
  • [43] Obstacle avoidance technology of bionic quadruped robot based on multi-sensor information fusion
    韩宝玲
    张天
    罗庆生
    朱颖
    宋明辉
    JournalofBeijingInstituteofTechnology, 2016, 25 (04) : 448 - 454
  • [44] A robot self-locating method with multi-sensor information fusion
    Qi, XH
    Shan, GL
    Wang, CP
    ISTM/2001: 4TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1 AND 2, CONFERENCE PROCEEDINGS, 2001, : 771 - 773
  • [45] Multi-sensor information fusion in Internet of Vehicles based on deep learning: A review
    Tian, Di
    Li, Jiabo
    Lei, Jingyuan
    NEUROCOMPUTING, 2025, 614
  • [46] AGV System Based on Multi-sensor Information Fusion
    Yuan, Peijiang
    Chen, Dongdong
    Wang, Tianmiao
    Ma, Fucun
    Ren, Hengfei
    Liu, Yuanwei
    Tan, Huanjian
    2014 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C 2014), 2014, : 900 - 905
  • [47] A MULTI-SENSOR INFORMATION FUSION ALGORITHM BASED ON SVM
    Adu, Jian-Hua
    Hu, De-Kun
    Peng, Hui
    Tie, Ju-Hong
    2008 INTERNATIONAL CONFERENCE ON APPERCEIVING COMPUTING AND INTELLIGENCE ANALYSIS (ICACIA 2008), 2008, : 40 - +
  • [48] Information Fusion Based Filtering for Multi-Sensor System
    Wang Zhisheng
    Zhen Ziyang
    Hu Yong
    2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11, 2008, : 427 - 430
  • [49] Robust Tightly Coupled Pose Measurement Based on Multi-Sensor Fusion in Mobile Robot System
    Peng, Gang
    Lu, Zezao
    Peng, Jiaxi
    He, Dingxin
    Li, Xinde
    Hu, Bin
    SENSORS, 2021, 21 (16)
  • [50] On-Board Multi-Sensor Fusion Based Track Three-Dimensional Real-Time Construction Method
    Chen, Shiming
    Wang, Hao
    Liu, Junbo
    Zhao, Xinxin
    Wang, Le
    Wang, Shengchun
    Yu, Ning
    Jin, Linhan
    Wang, Wei
    ACTA OPTICA SINICA, 2025, 45 (04)