Indoor localization and 3D scene reconstruction for mobile robots using the Microsoft Kinect sensor

In this paper we present an approach to indoor localization and 3D scene reconstruction using the Microsoft Kinect sensor. The proposed system can simultaneously estimates the position and orientation of a hand-held Kinect and generates a dense 3D model of the indoor environment. Furthermore, the robustness and processing time for four different feature descriptors (SURF, ORB, Shi-Tomasi and FAST) are evaluated. The experiment results demonstrate that our system can robustly deal with complicated data in common indoor scenarios while running in semi-real-time.

[1]  A. Nuchter,et al.  6D SLAM with approximate data association , 2005, ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005..

[2]  Lina María Paz,et al.  Large-Scale 6-DOF SLAM With Stereo-in-Hand , 2008, IEEE Transactions on Robotics.

[3]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

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

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

[6]  Nicola Fioraio,et al.  Realtime Visual and Point Cloud SLAM Nicola Fioraio Willow Garage , 2011 .

[7]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[8]  Wolfram Burgard,et al.  OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems , 2010 .

[9]  Manolis I. A. Lourakis,et al.  SBA: A software package for generic sparse bundle adjustment , 2009, TOMS.

[10]  Stefano Soatto,et al.  Real-time 3D motion and structure of point features: a front-end system for vision-based control and interaction , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

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

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

[13]  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.

[14]  Robert C. Bolles,et al.  Outdoor Mapping and Navigation Using Stereo Vision , 2006, ISER.

[15]  Andrew J. Davison,et al.  Real-time simultaneous localisation and mapping with a single camera , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[16]  Sebastian Thrun,et al.  Robotic mapping: a survey , 2003 .

[17]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[18]  Gerhard Lakemeyer,et al.  Exploring artificial intelligence in the new millennium , 2003 .

[19]  David Nistér,et al.  Preemptive RANSAC for live structure and motion estimation , 2005, Machine Vision and Applications.

[20]  Maren Bennewitz,et al.  Humanoid robot localization in complex indoor environments , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Jennifer A. Scott,et al.  Algorithm 891: A Fortran virtual memory system , 2009, TOMS.

[22]  Edwin Olson,et al.  Fast iterative alignment of pose graphs with poor initial estimates , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[23]  Tom Drummond,et al.  Fusing points and lines for high performance tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[24]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

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

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

[27]  Changchang Wu,et al.  SiftGPU : A GPU Implementation of Scale Invariant Feature Transform (SIFT) , 2007 .

[28]  Frank Dellaert,et al.  Square Root SAM , 2005, Robotics: Science and Systems.

[29]  Frank Dellaert,et al.  iSAM: Incremental Smoothing and Mapping , 2008, IEEE Transactions on Robotics.

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

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

[32]  Tom Duckett,et al.  A multilevel relaxation algorithm for simultaneous localization and mapping , 2005, IEEE Transactions on Robotics.

[33]  Wolfram Burgard,et al.  Efficient estimation of accurate maximum likelihood maps in 3D , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[34]  Dieter Fox,et al.  RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments , 2010, ISER.

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

[36]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.