论文标题
自我监督的深层姿势校正可靠的视觉探测器
Self-Supervised Deep Pose Corrections for Robust Visual Odometry
论文作者
论文摘要
我们提出了一个自我保护的深度姿势校正（DPC）网络，该网络将姿势校正应用于视觉频能计估计器以提高其准确性。我们没有直接回归框架间的姿势变化，而是基于使用数据驱动的学习来回归姿势校正的先前工作，该姿势校正因违反建模假设而导致系统错误。我们的自我监督配方消除了对六度基础真理的任何要求，与期望相比，与有监督的方法相比，通常提高了总体导航准确性。通过广泛的实验，我们表明，我们的自我监管的DPC网络可以显着提高经典单眼和立体声音量估计器的性能，并实质上超过最先进的学习方法。
We present a self-supervised deep pose correction (DPC) network that applies pose corrections to a visual odometry estimator to improve its accuracy. Instead of regressing inter-frame pose changes directly, we build on prior work that uses data-driven learning to regress pose corrections that account for systematic errors due to violations of modelling assumptions. Our self-supervised formulation removes any requirement for six-degrees-of-freedom ground truth and, in contrast to expectations, often improves overall navigation accuracy compared to a supervised approach. Through extensive experiments, we show that our self-supervised DPC network can significantly enhance the performance of classical monocular and stereo odometry estimators and substantially out-performs state-of-the-art learning-only approaches.
