We propose a differentiable nonlinear least squares framework to account for uncertainty in relative pose estimation from feature correspondences. Specifically, we introduce a symmetric version of the probabilistic normal epipolar constraint, and an approach to estimate the covariance of feature positions by differentiating through the camera pose estimation procedure. We evaluate our approach on synthetic, as well as the KITTI and EuRoC real-world datasets. On the synthetic dataset, we confirm that our learned covariances accurately approximate the true noise distribution. In real world experiments, we find that our approach consistently outperforms state-of-the-art non-probabilistic and probabilistic approaches, regardless of the feature extraction algorithm of choice.

翻译：我们提出了一种可微非线性最小二乘框架，用于量化特征对应关系在相对位姿估计中的不确定性。具体而言，我们引入了对称形式的概率化法向极线约束，并通过微分相机位姿估计过程来估计特征位置的协方差。该方法在合成数据集以及KITTI和EuRoC真实世界数据集上进行了评估。在合成数据集中，我们验证了学习得到的协方差能准确逼近真实噪声分布。真实实验结果表明，无论选用何种特征提取算法，该方法均持续优于现有最优的非概率与概率方法。