LOW-COST WHEELED ROBOT-BORNE LASER SCANNING SYSTEM FOR INDOOR AND OUTDOOR 3D MAPPING APPLICATION

Abstract. Aiming to accomplish automatic and real-time three-dimensional mapping in both indoor and outdoor scenes, a low-cost wheeled robot-borne laser scanning system is proposed in this paper. The system includes a laser scanner, an inertial measurement unit, a modified turtlebot3 two-wheel differential chassis and etc. To achieve a globally consistent map, the system performs global trajectory optimization after detecting the loop closure. Experiments are undertaken in two typical indoor/outdoor scenes that is an underground car park and a road environment in the campus of Wuhan University. The point clouds acquired by the proposed system are quantitatively evaluated by comparing the derived point clouds with the ground truth data collected by a RIEGL VZ 400 laser scanner. The results present an accuracy of 90% points below 0.1 meter error in the tested scene, showing that its applicability and potential in indoor and mapping applications.

[1]  Sven Behnke,et al.  Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Frank Dellaert,et al.  Eliminating conditionally independent sets in factor graphs: A unifying perspective based on smart factors , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Xiangyu Wang,et al.  Integrating BIM and LiDAR for Real-Time Construction Quality Control , 2015, J. Intell. Robotic Syst..

[4]  Yangzi Cong,et al.  3D Forest Mapping Using A Low-Cost UAV Laser Scanning System: Investigation and Comparison , 2019, Remote. Sens..

[5]  Jonathan Li,et al.  Low Cost Multi-Sensor Robot Laser Scanning System and its Accuracy Investigations for Indoor Mapping Application , 2017 .

[6]  G. Hunter,et al.  DEVELOPMENT OF A COMMERCIAL LASER SCANNING MOBILE MAPPING SYSTEM-STREETMAPPER , 2006 .

[7]  Jianxiong Xiao,et al.  Semantic alignment of LiDAR data at city scale , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Chen Liu,et al.  FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans , 2018, ECCV.

[9]  Cyrill Stachniss,et al.  Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments , 2018, Robotics: Science and Systems.

[10]  Jean-Emmanuel Deschaud,et al.  IMLS-SLAM: Scan-to-Model Matching Based on 3D Data , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Yi Lin,et al.  A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements , 2010 .

[12]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[13]  George E. Brown The future of remote sensing , 1989 .

[14]  Qingquan Li,et al.  A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems , 2015, Remote. Sens..

[15]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.