D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry

TL;DR

D3VO introduces a deep learning-based monocular visual odometry framework that estimates depth, pose, and uncertainty simultaneously, significantly improving accuracy over traditional methods and matching stereo/LiDAR performance.

Contribution

The paper presents a novel self-supervised depth network and integrates depth, pose, and uncertainty into a direct visual odometry system, enhancing monocular odometry accuracy.

Findings

Outperforms state-of-the-art self-supervised depth estimation networks.

Achieves superior monocular VO results on KITTI and EuRoC datasets.

Comparable to stereo/LiDAR odometry and visual-inertial methods.

Abstract

We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. In particular, it aligns the training image pairs into similar lighting condition with predictive brightness transformation parameters. Besides, we model the photometric uncertainties of pixels on the input images, which improves the depth estimation accuracy and provides a learned weighting function for the photometric residuals in direct (feature-less) visual odometry. Evaluation results show that the proposed network outperforms state-of-the-art self-supervised depth estimation networks. D3VO tightly incorporates the predicted depth, pose and uncertainty into a direct visual odometry method to boost both the front-end tracking as well as the back-end non-linear optimization. We evaluate D3VO in terms of monocular visual odometry on both the KITTI odometry benchmark and the EuRoC MAV dataset.The results show that D3VO outperforms state-of-the-art traditional monocular VO methods by a large margin. It also achieves comparable results to state-of-the-art stereo/LiDAR odometry on KITTI and to the state-of-the-art visual-inertial odometry on EuRoC MAV, while using only a single camera.