Improved Signed Distance Function for 2D Real-time SLAM and Accurate Localization

Accurate mapping and localization are very important for many industrial robotics applications. In this paper, we propose an improved Signed Distance Function (SDF) for both 2D SLAM and pure localization to improve the accuracy of mapping and localization. To achieve this goal, firstly we improved the back-end mapping to build a more accurate SDF map by extending the update range and building free space, etc. Secondly, to get more accurate pose estimation for the front-end, we proposed a new iterative registration method to align the current scan to the SDF submap by removing random outliers of laser scanners. Thirdly, we merged all the SDF submaps to produce an integrated SDF map for highly accurate pure localization. Experimental results show that based on the merged SDF map, a localization accuracy of a few millimeters (5mm) can be achieved globally within the map. We believe that this method is important for mobile robots working in scenarios where high localization accuracy matters.

[1]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

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

[3]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera , 2011, UIST.

[4]  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.

[5]  Stefan Kohlbrecher,et al.  A flexible and scalable SLAM system with full 3D motion estimation , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[6]  Dieter Fox,et al.  KLD-Sampling: Adaptive Particle Filters , 2001, NIPS.

[7]  Edwin Olson,et al.  Real-time correlative scan matching , 2009, 2009 IEEE International Conference on Robotics and Automation.

[8]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Wolfram Burgard,et al.  Efficient Sparse Pose Adjustment for 2D mapping , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Igor Skrjanc,et al.  EKF-Based Localization of a Wheeled Mobile Robot in Structured Environments , 2011, J. Intell. Robotic Syst..

[11]  脇元 修一,et al.  IEEE International Conference on Robotics and Automation (ICRA) におけるフルードパワー技術の研究動向 , 2011 .

[12]  Wolfram Burgard,et al.  On the position accuracy of mobile robot localization based on particle filters combined with scan matching , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  John J. Leonard,et al.  The MIT Stata Center dataset , 2013, Int. J. Robotics Res..

[14]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[15]  Jürgen Sturm,et al.  2D-SDF-SLAM: A signed distance function based SLAM frontend for laser scanners , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[16]  L. Guttman,et al.  Statistical Adjustment of Data , 1944 .

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

[18]  Andrea Censi,et al.  An ICP variant using a point-to-line metric , 2008, 2008 IEEE International Conference on Robotics and Automation.

[19]  Jari Saarinen,et al.  Normal distributions transform Monte-Carlo localization (NDT-MCL) , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.