Accurate Map-Based RGB-D SLAM for Mobile Robots

In this paper we present and evaluate a map-based RGB-D SLAM (Simultaneous Localization and Mapping) system employing a novel idea of combining efficient visual odometry and a persistent map of 3D point features used to jointly optimize the sensor (robot) poses and the feature positions. The optimization problem is represented as a factor graph. The SLAM system consists of a front-end that tracks the sensor frame-by-frame, extracts point features, and associates them with the map, and a back-end that manages and optimizes the map. We propose a robust approach to data association, which combines efficient selection of candidate features from the map, matching of visual descriptors guided by the sensor pose prediction from visual odometry, and verification of the associations in both the image plane and 3D space. The improved accuracy and robustness is demonstrated on publicly available data sets.

[1]  Daniel Cremers,et al.  Robust odometry estimation for RGB-D cameras , 2013, 2013 IEEE International Conference on Robotics and Automation.

[2]  Piotr Skrzypczynski,et al.  Precise self-localization of a walking robot on rough terrain using parallel tracking and mapping , 2013, Ind. Robot.

[3]  Ethan Rublee,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[4]  Paul Newman,et al.  Accelerating FAB-MAP With Concentration Inequalities , 2010, IEEE Transactions on Robotics.

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

[6]  Andrew J. Davison,et al.  A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Michal R. Nowicki,et al.  On the Performance of Pose-Based RGB-D Visual Navigation Systems , 2014, ACCV.

[8]  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.

[9]  Daniel Cremers,et al.  Submap-Based Bundle Adjustment for 3D Reconstruction from RGB-D Data , 2014, GCPR.

[10]  Andreas Zell,et al.  Efficient onbard RGBD-SLAM for autonomous MAVs , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Kurt Konolige,et al.  Double window optimisation for constant time visual SLAM , 2011, 2011 International Conference on Computer Vision.

[12]  Wolfram Burgard,et al.  3-D Mapping With an RGB-D Camera , 2014, IEEE Transactions on Robotics.

[13]  Michal R. Nowicki,et al.  Combining photometric and depth data for lightweight and robust visual odometry , 2013, 2013 European Conference on Mobile Robots.

[14]  Piotr Skrzypczynski,et al.  The importance of measurement uncertainty modelling in the feature-based RGB-D SLAM , 2015, 2015 10th International Workshop on Robot Motion and Control (RoMoCo).

[15]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[16]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Hirochika Inoue,et al.  Using visual odometry to create 3D maps for online footstep planning , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[18]  John J. Leonard,et al.  Robust real-time visual odometry for dense RGB-D mapping , 2013, 2013 IEEE International Conference on Robotics and Automation.

[19]  Óscar Martínez Mozos,et al.  A comparative evaluation of interest point detectors and local descriptors for visual SLAM , 2010, Machine Vision and Applications.

[20]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[21]  Luigi di Stefano,et al.  SlamDunk: Affordable Real-Time RGB-D SLAM , 2014, ECCV Workshops.

[22]  S. Umeyama,et al.  Least-Squares Estimation of Transformation Parameters Between Two Point Patterns , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Wolfram Burgard,et al.  G2o: A general framework for graph optimization , 2011, 2011 IEEE International Conference on Robotics and Automation.

[24]  Wolfram Burgard,et al.  A benchmark for the evaluation of RGB-D SLAM systems , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Giorgio Grisetti,et al.  Robust optimization of factor graphs by using condensed measurements , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.