MotionHint：運動制約を考慮した自己教師あり単眼視覚オドメトリ
MotionHint: Self-Supervised Monocular Visual Odometry with Motion Constraints
カメラの次フレーム姿勢と不確実性を予測するニューラルネットPPnetを導入し、運動損失を加えることで既存の自己教師あり単眼視覚オドメトリの局所解問題を緩和し、KITTIで最大28.73%のATE改善を達成した。
著者: Cong Wang, Yu-Ping Wang, Dinesh Manocha
分類: cs.CV, cs.RO
原文アブストラクト
We present a novel self-supervised algorithm named MotionHint for monocular visual odometry (VO) that takes motion constraints into account. A key aspect of our approach is to use an appropriate motion model that can help existing self-supervised monocular VO (SSM-VO) algorithms to overcome issues related to the local minima within their self-supervised loss functions. The motion model is expressed with a neural network named PPnet. It is trained to coarsely predict the next pose of the camera and the uncertainty of this prediction. Our self-supervised approach combines the original loss and the motion loss, which is the weighted difference between the prediction and the generated ego-motion. Taking two existing SSM-VO systems as our baseline, we evaluate our MotionHint algorithm on the standard KITTI benchmark. Experimental results show that our MotionHint algorithm can be easily applied to existing open-sourced state-of-the-art SSM-VO systems to greatly improve the performance by reducing the resulting ATE by up to 28.73%.
