Multi-Sensor Fusion for A Brain-Inspired SLAM System

被引：0
|
作者：
Zhang, Houzhan [1 ]
Tang, Huajin [1 ]
Yan, Rui [1 ]
机构：
[1] Sichuan Univ, Coll Comp Sci, Neuromorph Comp Res Ctr, Chengdu, Peoples R China
来源：
CONFERENCE PROCEEDINGS OF 2019 5TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR) | 2019年
关键词：
simultaneous localization and mapping; multi-sensor fusion; RatSLAM; cognitive map; PATH-INTEGRATION; MAP;
D O I：
10.1109/iccar.2019.8813400
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A brain-inspired simultaneous localization and mapping (SLAM) system, called RatSLAM, that requires information of odometry and visual scenes traveled before, is used to construct a cognitive map for a mobile robot. While existing RatSLAM systems, that use raw odometry, easily suffer from the problem of low accuracy of maps in complex environments. In this paper, we employ a multi-sensor fusion method to provide better odometry for RatSLAM system. Experiment results demonstrate that the proposed system, based on multi-sensor fusion, show significant improvements on the cognitive mapping results. Thus, the proposed system is able to construct more precise cognitive maps.
引用
收藏
页码：619 / 623
页数：5
相关论文
共 50 条
  • [31] A System for Activity Recognition Using Multi-Sensor Fusion
    Gao, Lei
    Bourke, Alan K.
    Nelson, John
    2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2011, : 7869 - 7872
  • [32] Calibration of multi-sensor fusion for autonomous vehicle system
    Lu, Yongkang
    Zhong, Wenjian
    Li, Yanzhou
    INTERNATIONAL JOURNAL OF VEHICLE DESIGN, 2023, 91 (1-3) : 248 - 262
  • [33] Target Tracking System for Multi-sensor Data Fusion
    Ma, Ke
    Zhang, Hanguang
    Wang, Rentao
    Zhang, Zhimin
    PROCEEDINGS OF 2017 IEEE 2ND INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC), 2017, : 1768 - 1772
  • [34] Multi-sensor fusion UAV dynamic landing system
    Zhang Z.-H.
    Sun W.
    Zhao C.-Y.
    Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2017, 25 : 151 - 159
  • [35] Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction
    Cai, Yiyi
    Ou, Yang
    Qin, Tuanfa
    SENSORS, 2024, 24 (07)
  • [36] Observable Degree Analysis for Multi-Sensor Fusion System
    Hu, Zhentao
    Chen, Tianxiang
    Ge, Quanbo
    Wang, Hebin
    SENSORS, 2018, 18 (12)
  • [37] Fuzzy Reliability Analysis of A Multi-sensor Fusion System
    Jiang, MingHua
    Hu, Ming
    Peng, Tao
    Ding, YiXiang
    FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 3, PROCEEDINGS, 2008, : 81 - 84
  • [38] Multi-Sensor Fusion Using Evidential SLAM for Navigating a Probe through Deep Ice
    Clemens, Joachim
    Reineking, Thomas
    BELIEF FUNCTIONS: THEORY AND APPLICATIONS (BELIEF 2014), 2014, 8764 : 339 - 347
  • [39] Mobile robot positioning method based on multi-sensor information fusion laser SLAM
    Zhi-Xiang Liu
    Chun-Xue Xie
    Miao Xie
    Jun Mao
    Cluster Computing, 2019, 22 : 5055 - 5061
  • [40] 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
12345