Abstract
In this paper, we propose a method for egomotion estimation of an indoor mobile robot under planar motion with an RGB-D camera. Our approach mainly deals with the corridor-like structured scenarios and uses the prior knowledge of the environment: when at least one vertical plane is detected using the depth data, egomotion is estimated with one normal of the vertical plane and one point; when there are no vertical planes, a 2-point homography-based algorithm using only point correspondences is presented for the egomotion estimation. The proposed method then is used in a frame-to-frame visual odometry framework. We evaluate our algorithm on the synthetic data and show the application on the real-world data. The experiments show that the proposed approach is efficient and robust enough for egomotion estimation in the Manhattan-like environments compared with the state-of-the-art methods.
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Mu, X., Hou, Z., Zhang, Y. (2019). Egomotion Estimation Under Planar Motion with an RGB-D Camera. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_6
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