DLL: Direct LIDAR Localization. A map-based localization approach for aerial robots

被引:20
|
作者
Caballero, Fernando [1 ]
Merino, Luis [2 ]
机构
[1] Univ Seville, Serv Robot Lab, Seville, Spain
[2] Univ Pablo Olavide, Serv Robot Lab, Seville, Spain
关键词
REGISTRATION; ICP;
D O I
10.1109/IROS51168.2021.9636501
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents DLL, a fast direct map-based localization technique using 3D LIDAR for its application to aerial robots. DLL implements a point cloud to map registration based on non-linear optimization of the distance of the points and the map, thus not requiring features, neither point correspondences. Given an initial pose, the method is able to track the pose of the robot by refining the predicted pose from odometry. Through benchmarks using real datasets and simulations, we show how the method performs much better than Monte-Carlo localization methods and achieves comparable precision to other optimization-based approaches but running one order of magnitude faster. The method is also robust under odometric errors. The approach has been implemented under the Robot Operating System (ROS), and it is publicly available.
引用
收藏
页码:5491 / 5498
页数:8
相关论文
共 50 条
  • [1] Map-based Robust Localization for Indoor Mobile Robots
    Song Jianchao
    Zhang Xuebo
    Sun Lei
    Liu Jingtai
    PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017), 2017, : 6945 - 6950
  • [2] A 2.5D Map-Based Mobile Robot Localization via Cooperation of Aerial and Ground Robots
    Nam, Tae Hyeon
    Shim, Jae Hong
    Cho, Young Im
    SENSORS, 2017, 17 (12)
  • [3] Detection of Feature Areas for Map-based Localization Using LiDAR Descriptors
    Hungar, Constanze
    Fricke, Jenny
    Jurgens, Stefan
    Koster, Frank
    2019 16TH WORKSHOP ON POSITIONING, NAVIGATION AND COMMUNICATIONS (WPNC 2019), 2019,
  • [4] A Dependence Maximization Approach towards Street Map-based Localization
    Irie, Kiyoshi
    Sugiyama, Masashi
    Tomono, Masahiro
    2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2015, : 3721 - 3728
  • [5] Topological map-based approach for localization and mapping memory optimization
    Aguiar, Andre S.
    dos Santos, Filipe N.
    Santos, Luis C.
    Sousa, Armando J.
    Boaventura-Cunha, Jose
    JOURNAL OF FIELD ROBOTICS, 2023, 40 (03) : 447 - 466
  • [6] An Efficient Probabilistic Occupancy Map-Based People Localization Approach
    Lin, Yen-Shuo
    Chen, Hua-Tsung
    Chuang, Jen-Hui
    2015 VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), 2015,
  • [7] Map-based localization for mobile robots in high-occluded and dynamic environments
    Wang, Yong
    Chen, Weidong
    Wang, Jingchuan
    INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION, 2014, 41 (03): : 241 - 252
  • [8] Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localization
    Ahmed, Syed Zeeshan
    Saputra, Vincensius Billy
    Verma, Saurab
    Zhang, Kun
    Adiwahono, Albertus Hendrawan
    2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2019, : 1614 - 1619
  • [9] Map-Based Localization Method for Autonomous Vehicles Using 3D-LIDAR
    Wang, Liang
    Zhang, Yihuan
    Wang, Jun
    IFAC PAPERSONLINE, 2017, 50 (01): : 276 - 281
  • [10] Efficient LiDAR/inertial-based localization with prior map for autonomous robots
    Song, Jian
    Chen, Yutian
    Liu, Xun
    Zheng, Nan
    INTELLIGENT SERVICE ROBOTICS, 2024, 17 (02) : 119 - 133