ABSTRACT
In visual odometry applications, tracking of features in a video sequence greatly impacts the accuracy of ego-motion estimation. A robust visual tracking has to take into account either geometric or photometric conditions to exclude outliers in the stage of motion estimation. In this work we propose a novel method to adaptively constrain the matching of features in time sequence. The control of matching is based on two major Euclidean properties, namely scale and angle. The scale constraint refers to the disparity image, and the angular criterion is derived from epipolar geometry. The proposed method is tested with two selected sequences from KITTI visual odometry benchmark datasets. Experimental results indicate promising improvement is attainable over a conventional feature tracker for visual odometry.
- Moravec, H. 1980. Obstacle Avoidance and Navigation in The Real World by A Seeing Robot Rover. In Tech. Report CMU-RITR-80-03 (Sep. 1980). Robotics Institute. Carnegie Mellon University.Google ScholarDigital Library
- Shi, J. and Tomasi, C. 1944. Good Features to Track. In Proc. International Conference on Pattern Recognition (June. 1994). 539--600.Google Scholar
- Kitt, B., Geiger, A., and Lategahn, H. 2010. Visual Odometry Based on Stereo Image Sequences with RANSAC-based Outlier Rejection Scheme. In Proc. Intelligent Vehicles Symposium (June. 2010). 486--492.Google Scholar
- Badino, H., Yamamoto, A., and Kanade, T. 2013. Visual Odometry by Multi-frame Feature Integration. In Proc. First International Workshop on Computer Vision for Autonomous Driving at ICCV. Google ScholarDigital Library
- Bellavia, F., Fanfani, M., Pazzaglia, F., and Colombo, C. 2013. Robust Selective Stereo SLAM without Loop Closure and Bundle Adjustment. In Proc. 17th International Conference on Image Analysis and Processing (ICIAP'13). 462--471.Google Scholar
- Olson, C. F., Matthies, L. H., Schoppers, M., and Maimone, M. W. 2003. Rover Navigation using Stereo Ego-motion. Robotics and Autonomous Systems. 43, 4 (June. 2003), 215--229.Google ScholarCross Ref
- Pollefeys, H., Nister, D., Frahm, J. M., Akbarzadeh, A., Mordohai, P., Clipp, B., Engels, C., Gallup, D., Kim, S. J., Merrell, P., Salmi, C., Sinha, S., Talton, B., Wang, L., Yang, Q., Stewenius, H., Yang, R., Welch, G., and Towles, H. 2000. Detailed Real-time Urban 3D Reconstruction from Video. International Journal of Computer Vision (IJCV). 78, 2-3 (July. 2008), 143--167. Google ScholarDigital Library
- Scaramuzza, D. and Fraundorfer, F. 2011. Visual Odometry Part I: The First 30 Years and Fundamentals. IEEE Robotics and Automation Society. 18, 4, 80--92.Google ScholarCross Ref
- Zhang, Z. 2000. A Flexible New Technique for Camera Calibration. In Proc. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22, 11, 1330--1334. Google ScholarDigital Library
- Hartley, R. I. and Ziessman, A. Multiple View Geometry in Computer Vision. 2nd edition. Cambridge University Press. ISBN: 0521540518. Google ScholarDigital Library
- Fusiello, A., Trucco, E., and Verri, A. 2000. A Compact Algorithm for Rectification of Stereo Pairs. Machine Vision and Applications. Springer-Verlag. 12, 16--22. Google ScholarDigital Library
- Hirschmüller, H. 2005. Accurate and Efficient Stereo Processing by Semi-global Matching and Mutual Information. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2, 807--814. Google ScholarDigital Library
- Lepetit, V., Moreno-Noguer, F., and Fua, P. 2009. EPnP: An Accurate O(n) Solution to the PnP Problem. International Journal of Computer Vision. 81, 2, 155--166. Google ScholarDigital Library
- Horn, B. K. P. 1987. Closed-form Solution of Absolute Orientation using Unit Quaternions. Journal of the Optical Society of America A. 4, 4, 629--642, 1987.Google ScholarCross Ref
- Lourakis, M. I. A. and Argyros, A. A. 2009. SBA: A software package for generic sparse bundle adjustment. Journal of ACM Transactions on Mathematical Software. 36, 1, 1--30. Google ScholarDigital Library
- Lowe, D. G. 2004. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision (IJCV). 60, 2, 91--110. Google ScholarDigital Library
- Fritsch, J., Kuehnl, T., and Geiger, A. 2013. A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms. In Proc. International Conference on Intelligent Transportation Systems 2013.Google Scholar
- The KITTI Vision Benchmark Suite (online) http://www.cvlibs.net/datasets/kitti/eval_odometry.php {accessed May 2014}Google Scholar
Index Terms
- Visual Odometry with Improved Adaptive Feature Tracking
Recommendations
Semi-dense Visual Odometry for a Monocular Camera
ICCV '13: Proceedings of the 2013 IEEE International Conference on Computer VisionWe propose a fundamentally novel approach to real-time visual odometry for a monocular camera. It allows to benefit from the simplicity and accuracy of dense tracking - which does not depend on visual features - while running in real-time on a CPU. The ...
Visual Odometry by Multi-frame Feature Integration
ICCVW '13: Proceedings of the 2013 IEEE International Conference on Computer Vision WorkshopsThis paper presents a novel stereo-based visual odometry approach that provides state-of-the-art results in real time, both indoors and outdoors. Our proposed method follows the procedure of computing optical flow and stereo disparity to minimize the re-...
Real-time Quadrifocal Visual Odometry
In this paper we describe a new image-based approach to tracking the six-degree-of-freedom trajectory of a stereo camera pair. The proposed technique estimates the pose and subsequently the dense pixel matching between temporal image pairs in a sequence ...
Comments