Robust 2D Indoor Localization Through Laser SLAM and Visual SLAM Fusion

An approach of robust localization for mobile robot working in indoor is proposed in this paper. A novel method for laser SLAM and visual SLAM fusion is introduced to provide robust localization. This architecture can be applied to a situation where any two kinds of laser-based SLAM and monocular camera-based SLAM can be fused together instead of being limited to single specific SLAM algorithm. While laser-based SLAM and monocular camera-based SLAM have their own strengths and drawbacks, the integration of these two kinds of SLAM algorithm can then promote the algorithmic effectiveness. Instead of using feature matching methods to achieve fusion procedure, trajectories matching is proposed with an attempt to achieve the generalization over all different kinds of SLAM algorithms, since localization is a natural function associated with any SLAM algorithm. It turns out that the hereby proposed approach is very lightweight during the run time, and the calculation can run in real-time without unnecessary computation waste. The experimental results show the localization error in terms of the real distance can be less than 5%. Furthermore, through the experiment the proposed system can be shown able to improve the localization when the sensors are not very powerful.

[1]  Hauke Strasdat,et al.  Scale Drift-Aware Large Scale Monocular SLAM , 2010, Robotics: Science and Systems.

[2]  Hugh F. Durrant-Whyte,et al.  CRF-Matching: Conditional Random Fields for Feature-Based Scan Matching , 2007, Robotics: Science and Systems.

[3]  Ricardo O. Carelli,et al.  Ultra Wide-Band Localization and SLAM: A Comparative Study for Mobile Robot Navigation , 2011, Sensors.

[4]  Yun Shi,et al.  GPS-Supported Visual SLAM with a Rigorous Sensor Model for a Panoramic Camera in Outdoor Environments , 2012, Sensors.

[5]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[6]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[7]  Hyun Myung,et al.  Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor , 2014, Sensors.

[8]  Tao Zhang,et al.  A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment , 2011, Sensors.

[9]  Daniel Cremers,et al.  LSD-SLAM: Large-Scale Direct Monocular SLAM , 2014, ECCV.

[10]  Hyun Myung,et al.  2D Image Feature-Based Real-Time RGB-D 3D SLAM , 2012, RiTA.

[11]  Frank Dellaert,et al.  Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing , 2006, Int. J. Robotics Res..

[12]  Rodrigo Munguía,et al.  A Robust Approach for a Filter-Based Monocular Simultaneous Localization and Mapping (SLAM) System , 2013, Sensors.

[13]  Rodrigo Munguía,et al.  Monocular SLAM for Autonomous Robots with Enhanced Features Initialization , 2014, Sensors.

[14]  Xinzheng Zhang,et al.  Sensor Fusion of Monocular Cameras and Laser Rangefinders for Line-Based Simultaneous Localization and Mapping (SLAM) Tasks in Autonomous Mobile Robots , 2012, Sensors.

[15]  Hyun Myung,et al.  Magnetic field constraints and sequence-based matching for indoor pose graph SLAM , 2015, Robotics Auton. Syst..

[16]  Edward H. Adelson,et al.  PYRAMID METHODS IN IMAGE PROCESSING. , 1984 .

[17]  Joachim Hertzberg,et al.  Three‐dimensional mapping with time‐of‐flight cameras , 2009, J. Field Robotics.

[18]  Lino Marques,et al.  Speeding up rao-blackwellized particle filter SLAM with a multithreaded architecture , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[20]  Seung-Mok Lee,et al.  DV-SLAM (Dual-Sensor-Based Vector-Field SLAM) and Observability Analysis , 2015, IEEE Transactions on Industrial Electronics.

[21]  Davide Scaramuzza,et al.  SVO: Fast semi-direct monocular visual odometry , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Wolfram Burgard,et al.  Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[23]  H. C. Longuet-Higgins,et al.  A computer algorithm for reconstructing a scene from two projections , 1981, Nature.

[24]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Dieter Fox,et al.  RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments , 2012, Int. J. Robotics Res..

[26]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

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

[28]  Yuwei Chen,et al.  NAVIS-An UGV Indoor Positioning System Using Laser Scan Matching for Large-Area Real-Time Applications , 2014, Sensors.

[29]  Hyun Myung,et al.  GPU-based real-time RGB-D 3D SLAM , 2012, 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).