VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals

• URL: http://arxiv.org/abs/2104.06789v1
• Date: Wed, 14 Apr 2021 11:39:19 GMT
• Title: VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals
• Authors: Zhixiang Min, Yiding Yang, Enrique Dunn
• Abstract summary: We develop a generalized-EM formulation for the joint inference of camera motion, pixel depth, and motion-track confidence. Our method achieved top-ranking results on both TUM RGB-D and KITTI odometry benchmarks. Our open-sourced implementation is inherently GPU-friendly with only linear computational and storage growth.
• Score: 18.739522634776062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences. We define our problem as a probabilistic model and develop a generalized-EM formulation for the joint inference of camera motion, pixel depth, and motion-track confidence. Contrary to traditional methods assuming Gaussian-distributed observation errors, we supervise our inference framework under an (empirically validated) adaptive log-logistic distribution model. Moreover, the log-logistic residual model generalizes well to different state-of-the-art optical flow methods, making our approach modular and agnostic to the choice of optical flow estimators. Our method achieved top-ranking results on both TUM RGB-D and KITTI odometry benchmarks. Our open-sourced implementation is inherently GPU-friendly with only linear computational and storage growth.

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