Abstract
Visual odometry can be used to estimate the pose of a robot from current and recent video frames. A problem with these methods is that they drift over time due to the accumulation of estimation errors at each time-step. In this short paper we propose and briefly demonstrate the potential benefit of using prior 2D, top-down map information combined with multiple hypothesis particle filtering to correct visual odometry estimates. The results demonstrate a substantial improvement in robustness and accuracy over the sole use of visual odometry.
This work is supported by the UK’s Engineering and Physical Sciences Research Council (EPSRC) Programme Grant EP/S016813/1 Pervasive Sensing for Buried Pipes (Pipebots) https://pipebots.ac.uk.
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Edwards, S., Mihaylova, L., Aitken, J.M., Anderson, S. (2021). Toward Robust Visual Odometry Using Prior 2D Map Information and Multiple Hypothesis Particle Filtering. In: Fox, C., Gao, J., Ghalamzan Esfahani, A., Saaj, M., Hanheide, M., Parsons, S. (eds) Towards Autonomous Robotic Systems. TAROS 2021. Lecture Notes in Computer Science(), vol 13054. Springer, Cham. https://doi.org/10.1007/978-3-030-89177-0_19
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DOI: https://doi.org/10.1007/978-3-030-89177-0_19
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