ロバストな視覚オドメトリのための自己教師あり深層ポーズ補正
Self-Supervised Deep Pose Corrections for Robust Visual Odometry
視覚オドメトリ推定器の系統誤差を補正する自己教師あり深層ポーズ補正ネットワークを提案し、6自由度の正解データなしで単眼・ステレオオドメトリの精度を大幅に向上させた。
著者: Brandon Wagstaff, Valentin Peretroukhin, Jonathan Kelly
分類: cs.RO
原文アブストラクト
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.
