VOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals

Zhixiang Min, Yiding Yang, Enrique Dunn; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 4898-4909

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.

Related Material


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[bibtex]
@InProceedings{Min_2020_CVPR,
author = {Min, Zhixiang and Yang, Yiding and Dunn, Enrique},
title = {VOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}