Abstract
The availability of affordable RGB-D cameras which provide color and depth data at high data rates, such as Microsoft MS Kinect, poses a challenge to the limited resources of the computers onboard autonomous robots. Estimating the sensor trajectory, for example, is a key ingredient for robot localization and SLAM (Simultaneous Localization And Mapping), but current computers can hardly handle the stream of measurements. In this paper, we propose an efficient and reliable method to estimate the 6D movement of an RGB-D camera (3 linear translations and 3 rotation angles) of a moving RGB-D camera. Our approach is based on visual features that are mapped to the three Cartesian coordinates (3D) using measured depth. The features of consecutive frames are associated in 3D and the sensor pose increments are obtained by solving the resulting linear least square minimization system. The main contribution of our approach is the definition of a filter setup that produces the most reliable features that allows for keeping track of the sensor pose with a limited number of feature points. We systematically evaluate our approach using ground truth from an external measurement systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Moravec, H.: Obstacle avoidance and navigation in the real world by a seeing robot rover. Tech. report and doctoral dissertation, Robotics Institute, Carnegie Mellon University, Stanford University, CMU-RI-TR-80-03 (1980)
Nister, D., Naroditsky, O., Bergen, J.: Visual odometry for ground vehicle applications. Journal of Field Robotics 23(1) (2006)
Davison, A.J.: Real-time simultaneous localization and mapping with a single camera. In: Proceedings of the International Conference on Computer Vision, Nice (2003)
Matties, L.: Dynamic Stereo Vision. PhD thesis, Dept. of Computer Science, Carnegie Mellon University, CMU-CS-89-195 (1989)
Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of Optical Flow Techniques. The International Journal of Computer Vision 12(1), 43–77 (1994)
Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proceedings of 7th International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
Einhorn, E., Schrter, C., Gross-M, H.-M.: Can’t Take my Eye on You: Attention-Driven Monocular Obstacle Detection and 3D Mapping. In: IROS 2010 (2010)
Lowe, D.G.: Distinctive image features from scale invariant keypoints. International Journal of Computer Vision 60(2), 91110 (2004)
Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Proc. of The Fourth Alvey Vision Conference, Manchester, pp. 147–151 (1988)
Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, Seattle (1993)
Klippenstein, J., Zhang, H.: Quantitative Evaluation of Feature Extractors for Visual SLAM. J Department of Computing Science University of Alberta Edmonton, AB, Canada T6G 2E8 (2007)
Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins (1996) ISBN 978-0-8018-5414-9
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Domínguez, S., Zalama, E., García-Bermejo, J.G., Worst, R., Behnke, S. (2013). Fast 6D Odometry Based on Visual Features and Depth. In: Lee, S., Yoon, KJ., Lee, J. (eds) Frontiers of Intelligent Autonomous Systems. Studies in Computational Intelligence, vol 466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35485-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-35485-4_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35484-7
Online ISBN: 978-3-642-35485-4
eBook Packages: EngineeringEngineering (R0)