Abstract
3D depth sensors such as LIDARs and RGB-D cameras have become a popular choice for indoor localization and mapping. However, due to the lack of direct frame-to-frame correspondences, the tracking traditionally relies on the iterative closest point technique which does not scale well with the number of points. In this paper, we build on top of more recent and efficient density distribution alignment methods, and notably push the idea towards a highly efficient and reliable solution for full 6DoF motion estimation with only depth information. We propose a divide-and-conquer technique during which the estimation of the rotation and the three degrees of freedom of the translation are all decoupled from one another. The rotation is estimated absolutely and drift-free by exploiting the orthogonal structure in man-made environments. The underlying algorithm is an efficient extension of the mean-shift paradigm to manifold-constrained multiple-mode tracking. Dedicated projections subsequently enable the estimation of the translation through three simple 1D density alignment steps that can be executed in parallel. An extensive evaluation on both simulated and publicly available real datasets comparing several existing methods demonstrates outstanding performance at low computational cost.
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Acknowledgement
The research leading to these results is supported by Australian Centre for Robotic Vision. The work is furthermore supported by ARC grants DE150101365. Yi Zhou acknowledges the financial support from the China Scholarship Council for his Ph.D. Scholarship No. 201406020098.
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Zhou, Y., Kneip, L., Rodriguez, C., Li, H. (2017). Divide and Conquer: Efficient Density-Based Tracking of 3D Sensors in Manhattan Worlds. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10115. Springer, Cham. https://doi.org/10.1007/978-3-319-54193-8_1
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