MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry
This work addresses robust pose estimation for autonomous systems in challenging conditions like varying illumination and feature density, representing an incremental improvement over prior learning-based methods.
The authors tackled the problem of improving stereo visual odometry (VO) by proposing MAC-VO, which uses learned metrics-aware uncertainty for keypoint selection and covariance modeling in pose optimization, resulting in outperformance over existing VO and some SLAM algorithms on public benchmarks in challenging environments.
We propose the MAC-VO, a novel learning-based stereo VO that leverages the learned metrics-aware matching uncertainty for dual purposes: selecting keypoint and weighing the residual in pose graph optimization. Compared to traditional geometric methods prioritizing texture-affluent features like edges, our keypoint selector employs the learned uncertainty to filter out the low-quality features based on global inconsistency. In contrast to the learning-based algorithms that model the scale-agnostic diagonal weight matrix for covariance, we design a metrics-aware covariance model to capture the spatial error during keypoint registration and the correlations between different axes. Integrating this covariance model into pose graph optimization enhances the robustness and reliability of pose estimation, particularly in challenging environments with varying illumination, feature density, and motion patterns. On public benchmark datasets, MAC-VO outperforms existing VO algorithms and even some SLAM algorithms in challenging environments. The covariance map also provides valuable information about the reliability of the estimated poses, which can benefit decision-making for autonomous systems.