Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras

TL;DR

Stereo DSO is a real-time, large-scale visual odometry method using stereo cameras that improves accuracy, robustness, and 3D reconstruction density by integrating static stereo constraints into a direct sparse optimization framework.

Contribution

It introduces a novel approach to incorporate static stereo constraints into direct sparse visual odometry, enhancing accuracy and robustness in large-scale environments.

Findings

Outperforms state-of-the-art methods in accuracy and robustness.

Provides more precise 3D reconstructions than previous dense/semi-dense methods.

Achieves real-time performance with high reconstruction density.

Abstract

We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras. It jointly optimizes for all the model parameters within the active window, including the intrinsic/extrinsic camera parameters of all keyframes and the depth values of all selected pixels. In particular, we propose a novel approach to integrate constraints from static stereo into the bundle adjustment pipeline of temporal multi-view stereo. Real-time optimization is realized by sampling pixels uniformly from image regions with sufficient intensity gradient. Fixed-baseline stereo resolves scale drift. It also reduces the sensitivities to large optical flow and to rolling shutter effect which are known shortcomings of direct image alignment methods. Quantitative evaluation demonstrates that the proposed Stereo DSO outperforms existing state-of-the-art visual odometry methods both in terms of tracking accuracy and robustness. Moreover, our method delivers a more precise metric 3D reconstruction than previous dense/semi-dense direct approaches while providing a higher reconstruction density than feature-based methods.