Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry

ICCV 2019 Shunkai LiFei XueXin WangZike YanHongbin Zha

We propose a self-supervised learning framework for visual odometry (VO) that incorporates correlation of consecutive frames and takes advantage of adversarial learning. Previous methods tackle self-supervised VO as a local structure from motion (SfM) problem that recovers depth from single image and relative poses from image pairs by minimizing photometric loss between warped and captured images... (read more)

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