Abstract
This paper demonstrates an application of a Simultaneous Localization and Mapping algorithm to localize a six-legged robot using data from a compact RGB-D sensor. The algorithm employs a new concept of combining fast Visual Odometry to track the sensor motion, and a map of 3-D point features and robot poses, which is then optimized. The focus of the paper is on evaluating the presented approach on a real walking robot under supervision of a motion registration system that provides ground truth trajectories. We evaluate the accuracy of the estimated robot trajectories applying the well-established methodologies of Relative Pose Error and Absolute Trajectory Error, and investigate the causes of accuracy degradation when the RGB-D camera is carried by a walking robot. Moreover, we demonstrate that the accuracy of robot poses is sufficient for dense environment mapping in 3-D.
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- 1.
Source code is available at https://github.com/LRMPUT/PUTSLAM/tree/release.
- 2.
Data set is publicly available at http://lrm.put.poznan.pl/putslam/.
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Acknowledgments
This work was financed by the Polish National Science Centre under decision DEC-2013/09/B/ST7/01583.
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Belter, D., Nowicki, M., Skrzypczyński, P. (2016). Evaluating Map-Based RGB-D SLAM on an Autonomous Walking Robot. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Challenges in Automation, Robotics and Measurement Techniques. ICA 2016. Advances in Intelligent Systems and Computing, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-319-29357-8_42
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DOI: https://doi.org/10.1007/978-3-319-29357-8_42
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