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Sep, 2017
LS-VO：学习稠密光学子空间用于鲁棒地视觉里程计估计
LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation
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Gabriele Costante, Thomas A. Ciarfuglia
TL;DR
本文提出了一种新颖的深度网络结构来解决相机自我运动估计问题，并使用自编码器网络找到光流场的非线性表示，从而显著提高了估计性能。
Abstract
This work proposes a novel
deep network architecture
to solve the
camera ego-motion estimation
problem. A motion estimation network generally learns features similar to
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