Uncertainty Estimation of Dense Optical Flow for Robust Visual Navigation

This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localisation and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling collision avoidance with the potential obstacles. Existing research has not fully utilised the uncertainty of the optical flow—at most, an isotropic Gaussian density model has been used. We estimate the full uncertainty of the optical flow and propose a new eight-point algorithm based on the statistical Mahalanobis distance. Combined with the pose-graph optimisation, the proposed method demonstrates enhanced robustness and accuracy for the public autonomous car dataset (KITTI) and aerial monocular dataset.

[1]  Eric Moulines,et al.  Comparison of resampling schemes for particle filtering , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[2]  Jonghyuk Kim,et al.  Robust linear pose graph-based SLAM , 2015, Robotics Auton. Syst..

[3]  Jia Xu,et al.  Accurate Optical Flow via Direct Cost Volume Processing , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  R. Siegwart,et al.  A UAV system for inspection of industrial facilities , 2013, 2013 IEEE Aerospace Conference.

[5]  Xavier Armangué,et al.  Overall view regarding fundamental matrix estimation , 2003, Image Vis. Comput..

[6]  Stephen E. Dunagan,et al.  PRECISION AGRICULTURE AS A COMMERCIAL APPLICATION FOR SOLAR-POWERED UNMANNED AERIAL VEHICLES , 2002 .

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

[8]  Robert E. Mahony,et al.  Robust Nonlinear Fusion of Inertial and Visual Data for position, velocity and attitude estimation of UAV , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[9]  Miguel A. Olivares-Méndez,et al.  Visual 3-D SLAM from UAVs , 2009, J. Intell. Robotic Syst..

[10]  Reprint of: Mahalanobis, P.C. (1936) "On the Generalised Distance in Statistics." , 2018, Sankhya A.

[11]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

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

[13]  Richard I. Hartley,et al.  In Defense of the Eight-Point Algorithm , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Zhengyou Zhang,et al.  Determining the Epipolar Geometry and its Uncertainty: A Review , 1998, International Journal of Computer Vision.

[15]  Julius Ziegler,et al.  StereoScan: Dense 3d reconstruction in real-time , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[16]  Andreas Geiger,et al.  Object scene flow for autonomous vehicles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Guido Morgenthal,et al.  Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures , 2014 .

[18]  Agathoniki Trigoni,et al.  Supporting Search and Rescue Operations with UAVs , 2010, 2010 International Conference on Emerging Security Technologies.

[19]  Clark C. Guest,et al.  High Accuracy Monocular SFM and Scale Correction for Autonomous Driving , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Rudolf Mester,et al.  Predictive monocular odometry (PMO): What is possible without RANSAC and multiframe bundle adjustment? , 2017, Image Vis. Comput..

[21]  Konstantin Kondak,et al.  Autonomous transportation and deployment with aerial robots for search and rescue missions , 2011, J. Field Robotics.

[22]  Cyrill Stachniss,et al.  Pose Graph Compression for Laser-Based SLAM , 2011, ISRR.

[23]  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.

[24]  Rudolf Mester,et al.  The Statistics of Driving Sequences -- And What We Can Learn from Them , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[25]  Jan Kautz,et al.  PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[26]  Vladlen Koltun,et al.  Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  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.

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

[29]  J. Flexas,et al.  UAVs challenge to assess water stress for sustainable agriculture , 2015 .

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

[31]  Jörg Stückler,et al.  Large-scale direct SLAM with stereo cameras , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[32]  Cordelia Schmid,et al.  EpicFlow: Edge-preserving interpolation of correspondences for optical flow , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Jonghyuk Kim,et al.  Robust Dense Optical Flow with Uncertainty for Monocular Pose-Graph SLAM , 2017 .

[34]  The National Institute of Sciences of India , 1963, Nature.

[35]  Andrew Zisserman,et al.  Robust computation and parametrization of multiple view relations , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[36]  Jianliang Tang,et al.  Complete Solution Classification for the Perspective-Three-Point Problem , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Xiaoou Tang,et al.  LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[38]  Robert E. Mahony,et al.  A Filter Formulation for Computing Real Time Optical Flow , 2016, IEEE Robotics and Automation Letters.