MAC-VO: メトリクス対応共分散を用いた学習ベースステレオ視覚オドメトリ
MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry
学習したマッチング不確かさをキーポイント選択とポーズグラフ最適化の重み付けに活用し、空間誤差と軸間相関を捉える共分散モデルを導入したステレオ視覚オドメトリ手法を提案。
著者: Yuheng Qiu, Yutian Chen, Zihao Zhang, Wenshan Wang, Sebastian Scherer
分類: cs.RO, cs.CV
原文アブストラクト
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.
