光度損失を超えて：自己教師あり自己運動推定のための幾何学的マッチング損失
Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation
自己教師あり視覚オドメトリにおいて、反射や遮蔽に弱い光度誤差の代わりにエピポーラ幾何で拘束したマッチング損失を導入し、KITTIで従来の教師なし自己運動推定を大きく上回った論文。
著者: Tianwei Shen, Zixin Luo, Lei Zhou, Hanyu Deng, Runze Zhang, Tian Fang, Long Quan
分類: cs.CV, cs.RO
原文アブストラクト
Accurate relative pose is one of the key components in visual odometry (VO) and simultaneous localization and mapping (SLAM). Recently, the self-supervised learning framework that jointly optimizes the relative pose and target image depth has attracted the attention of the community. Previous works rely on the photometric error generated from depths and poses between adjacent frames, which contains large systematic error under realistic scenes due to reflective surfaces and occlusions. In this paper, we bridge the gap between geometric loss and photometric loss by introducing the matching loss constrained by epipolar geometry in a self-supervised framework. Evaluated on the KITTI dataset, our method outperforms the state-of-the-art unsupervised ego-motion estimation methods by a large margin. The code and data are available at https://github.com/hlzz/DeepMatchVO.
