LiDAR教師あり学習による高精度ビジュアル自己位置推定
Topometric Localization with Deep Learning
LiDARベースの位置推定結果を教師データとして、視覚オドメトリとトポロジカル位置推定の2つの深層ネットワークを学習し、それらを統合最適化することで、安価なカメラのみでLiDAR並みの精度を実現した。
著者: Gabriel L. Oliveira, Noha Radwan, Wolfram Burgard, Thomas Brox
分類: cs.CV, cs.RO
原文アブストラクト
Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost-effective while their accuracy and reliability typically is inferior to LiDAR-based methods. In this work, we propose a vision-based localization approach that learns from LiDAR-based localization methods by using their output as training data, thus combining a cheap, passive sensor with an accuracy that is on-par with LiDAR-based localization. The approach consists of two deep networks trained on visual odometry and topological localization, respectively, and a successive optimization to combine the predictions of these two networks. We evaluate the approach on a new challenging pedestrian-based dataset captured over the course of six months in varying weather conditions with a high degree of noise. The experiments demonstrate that the localization errors are up to 10 times smaller than with traditional vision-based localization methods.
