This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localization and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling collision avoidance with the potential obstacles. Existing work has not fully utilized the uncertainty of the optical flow -- at most an isotropic Gaussian density model. We estimate the full uncertainty of the optical flow and propose a new eight-point algorithm based on the statistical Mahalanobis distance. Combined with the pose-graph optimization, the proposed method demonstrates enhanced robustness and accuracy for the public autonomous car dataset (KITTI) and aerial monocular dataset.

翻译：本文介绍了一种新颖的密集光学流算法,以解决地面或空中机器人单眼同步定位和绘图(SLAM)问题,高光学流能有效提供飞行器的自我移动,同时能够避免与潜在障碍发生碰撞。现有工作尚未充分利用光学流动的不确定性 -- -- 至多是一个异位高斯密度模型。我们估计光学流动的充分不确定性,并根据统计马哈拉诺比斯距离提出一个新的八点算法。与配置光学优化相结合,拟议方法显示公共自主汽车数据集(KITTI)和空中单子数据集的可靠性和准确性得到加强。