A Multisensor Data Fusion Approach for Simultaneous Localization and Mapping*

Simultaneous localization and mapping (SLAM) has been an emerging research topic in the fields of robotics, autonomous driving, and unmanned aerial vehicles over the past thirty years. State of the art SLAM research is often inaccessible for undergraduate student researchers due to expensive hardware and difficult software setup. We present a cost-friendly vehicle research platform and a robust implementation of SLAM. Our SLAM algorithm fuses visual stereo image and 2D light detection and ranging (Lidar) data and uses loop closure for accurate odometry estimation. Our algorithm is benchmarked against other popular SLAM algorithms using the publicly available KITTI dataset and shown to be very accurate. For educational purposes, we publicly share the models and code presented in this work*.

[1]  Wolfgang Hess,et al.  Real-time loop closure in 2D LIDAR SLAM , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Beatriz L. Boada,et al.  Local mapping from online laser Voronoi extraction , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[3]  Minoru Asada,et al.  Representing Global World Of A Mobile Robot With Relational Local Maps , 1988, IEEE International Workshop on Intelligent Robots.

[4]  Renaud Dubé,et al.  SegMatch: Segment based loop-closure for 3D point clouds , 2016, ArXiv.

[5]  Ahmet M. Kondoz,et al.  Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots , 2017, Sensors.

[6]  Dilbag Singh,et al.  Multisensor Data Fusion and Integration for Mobile Robots: A Review , 2014, ICRA 2014.

[7]  J. M. M. Montiel,et al.  ORB-SLAM: A Versatile and Accurate Monocular SLAM System , 2015, IEEE Transactions on Robotics.

[8]  Shi-Sheng Huang,et al.  Tightly-Coupled Monocular Visual-Odometric SLAM Using Wheels and a MEMS Gyroscope , 2018, IEEE Access.

[9]  John J. Leonard,et al.  Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age , 2016, IEEE Transactions on Robotics.

[10]  Joachim Hertzberg,et al.  An Explicit Loop Closing Technique for 6D SLAM , 2009, ECMR.

[11]  Ji Zhang,et al.  LOAM: Lidar Odometry and Mapping in Real-time , 2014, Robotics: Science and Systems.

[12]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[14]  Oskar von Stryk,et al.  Hector Open Source Modules for Autonomous Mapping and Navigation with Rescue Robots , 2013, RoboCup.

[15]  Chun C. Lai,et al.  Multisensor Fusion and Integration: Theories, Applications, and its Perspectives , 2011, IEEE Sensors Journal.

[16]  Ingemar J. Cox,et al.  Dynamic Map Building for an Autonomous Mobile Robot , 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.

[17]  Yoshua Nava Chocron.  Visual-LiDAR SLAM with loop closure , 2019 .

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

[19]  Paul Newman,et al.  SLAM-Loop Closing with Visually Salient Features , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.