MAC-VO：面向学习型立体视觉里程计的度量感知协方差
MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry
AI总结：
MAC-VO提出利用学习到的度量感知匹配不确定性进行关键点选择与协方差建模，并融入位姿图优化，从而在挑战性环境中提升立体视觉里程计的鲁棒性和位姿估计可靠性。
摘要：
我们提出 MAC-VO，一种新颖的基于学习的立体视觉里程计（VO），利用学习到的度量感知匹配不确定性实现双重目的：在关键点选择和位姿图优化中加权残差。与优先选择边缘等纹理丰富特征的傳統几何方法相比，我们的关键点选择器利用学习到的不确定性，基于全局不一致性过滤低质量特征。与为协方差建模尺度无关对角权重矩阵的基于学习算法不同，我们设计了一个度量感知协方差模型，以捕获关键点配准期间的空间误差以及不同轴之间的相关性。将该协方差模型集成到位姿图优化中，增强了位姿估计的鲁棒性和可靠性，特别是在光照变化、特征密度和运动模式具有挑战性的环境中。在公开基准数据集上，MAC-VO 在挑战性环境中优于现有的 VO 算法，甚至优于一些 SLAM 算法。协方差图还提供了有关估计位姿可靠性的宝贵信息，这有利于自主系统的决策。
英文摘要：
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.
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