Abstract
Estimating the ego-motion of a mobile robot has been traditionally achieved by means of encoder-based odometry. However, this method presents several drawbacks, such as the existence of accumulative drifts, its sensibility to slippage, and its limitation to planar environments. In this work we present an alternative method for estimating the incremental change in the robot pose from images taken by a stereo camera. In contrast to most previous approaches for 6D visual odometry, based on iterative, approximate methods, we propose here to employ an optimal closed-form formulation which is more accurate, efficient, and does not exhibit convergence problems. We also derive the expression for the covariance associated to this estimation, which enables the integration of our approach into vision-based SLAM frameworks. Additionally, our proposal combines highly-distinctive SIFT descriptors with the fast KLT feature tracker, thus achieving robust and efficient execution in real-time. To validate our research we provide experimental results for a real robot.
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Moreno, F.A., Blanco, J.L., González, J. (2007). An Efficient Closed-Form Solution to Probabilistic 6D Visual Odometry for a Stereo Camera. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_85
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DOI: https://doi.org/10.1007/978-3-540-74607-2_85
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