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The self-supervised learning-based depth and visual odometry (VO) estimators trained on monocular videos without ground truth have drawn significant attention recently. Prior works use photometric consistency as supervision, which is fragile under complex realistic environments due to illumination variations. More importantly, it suffers from scale inconsistency in the depth and pose estimation results. In this paper, robust geometric losses are proposed to deal with this problem. Specifically,doi:10.24963/ijcai.2020/134 dblp:conf/ijcai/XiongZZJLX20 fatcat:f34bauewyvgyrmaleppk63megy