Abstract
This paper presents improvements made to previous method for monocular teach-and-repeat navigation of mobile robots. The method is based on recording the position of image features in camera image, and moving the robot so their position matches during the recall. The method has shown good reliability, though requires odometry to perform well. This paper targets improvements of the method by replacement of a simple odometry by visual pose recognition approach. Thus, localization becomes independent of preceding pose computation. This prevents accumulation of error during the run of the algorithm.
A pose recognition method based on angle differences is presented herein. The substitution of odometry implies necessary adjustments to the aforementioned method to be used. Suitability of the method for pose recognition is evaluated experimentally. The method has shown to be feasible for the nav task, although the achieved accuracy is lower than the original method.
L. Přeučil and M. Kulich—This research was supported by the Grant Agency of the Czech Republic (GACR) with the grant no. 15-22731S entitled "Symbolic Regression for Reinforcement Learning in Continuous Spaces" and Technology Agency of the Czech Republic under the project no. TE01020197 “Centre for Applied Cybernetics”.
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Dörfler, M., Přeučil, L., Kulich, M. (2016). Vision-Based Pose Recognition, Application for Monocular Robot Navigation. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-319-27146-0_35
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