Robust Real-Time Visual Odometry with a Single Camera and an IMU

The increasing demand for real-time high-precision Visual Odometry systems as part of navigation and localization tasks has recently been driving research towards more versatile and scalable solutions. In this paper, we present a novel framework for combining the merits of inertial and visual data from a monocular camera to accumulate estimates of local motion incrementally and reliably reconstruct the trajectory traversed. We demonstrate the robustness and efficiency of our methodology in a scenario with challenging camera dynamics, and present a comprehensive evaluation against ground-truth data.

[1]  Amir Hashemi,et al.  A New Solution to the Relative Orientation Problem Using Only 3 Points and the Vertical Direction , 2009, Journal of Mathematical Imaging and Vision.

[2]  Kurt Konolige,et al.  Large-Scale Visual Odometry for Rough Terrain , 2007, ISRR.

[3]  Darius Burschka,et al.  Adaptive and Generic Corner Detection Based on the Accelerated Segment Test , 2010, ECCV.

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

[5]  Roland Siegwart,et al.  A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation , 2011, CVPR 2011.

[6]  Raffaello D'Andrea,et al.  A simple learning strategy for high-speed quadrocopter multi-flips , 2010, 2010 IEEE International Conference on Robotics and Automation.

[7]  Illah R. Nourbakhsh,et al.  Techniques for evaluating optical flow for visual odometry in extreme terrain , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[8]  Zuzana Kukelova,et al.  Closed-Form Solutions to Minimal Absolute Pose Problems with Known Vertical Direction , 2010, ACCV.

[9]  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.

[10]  Margarita Chli,et al.  Applying information theory to efficient SLAM , 2009 .

[11]  Reinhard Koch,et al.  Three-dimensional scene reconstruction from images , 2000, Electronic Imaging.

[12]  Jorge Dias,et al.  Relative Pose Calibration Between Visual and Inertial Sensors , 2007, Int. J. Robotics Res..

[13]  Sanjiv Singh,et al.  Motion Estimation from Image and Inertial Measurements , 2004, Int. J. Robotics Res..

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

[15]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

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

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

[18]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[19]  Andrew Howard,et al.  Real-time stereo visual odometry for autonomous ground vehicles , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Horst Bischof,et al.  A Duality Based Approach for Realtime TV-L1 Optical Flow , 2007, DAGM-Symposium.

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

[22]  Marc Pollefeys,et al.  A Minimal Case Solution to the Calibrated Relative Pose Problem for the Case of Two Known Orientation Angles , 2010, ECCV.

[23]  Roland Siegwart,et al.  A Toolbox for Easily Calibrating Omnidirectional Cameras , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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