合成データから新しい環境での自己位置推定を学習する手法
Learning to Localize in New Environments from Synthetic Training Data
合成データで学習し、未知のシーンにも汎化できる視覚的自己位置推定モデルを提案。階層的相関層や不確実性の活用により、従来の学習ベース・古典的手法を上回る性能を示した。
著者: Dominik Winkelbauer, Maximilian Denninger, Rudolph Triebel
分類: cs.RO, cs.CV
原文アブストラクト
Most existing approaches for visual localization either need a detailed 3D model of the environment or, in the case of learning-based methods, must be retrained for each new scene. This can either be very expensive or simply impossible for large, unknown environments, for example in search-and-rescue scenarios. Although there are learning-based approaches that operate scene-agnostically, the generalization capability of these methods is still outperformed by classical approaches. In this paper, we present an approach that can generalize to new scenes by applying specific changes to the model architecture, including an extended regression part, the use of hierarchical correlation layers, and the exploitation of scale and uncertainty information. Our approach outperforms the 5-point algorithm using SIFT features on equally big images and additionally surpasses all previous learning-based approaches that were trained on different data. It is also superior to most of the approaches that were specifically trained on the respective scenes. We also evaluate our approach in a scenario where only very few reference images are available, showing that under such more realistic conditions our learning-based approach considerably exceeds both existing learning-based and classical methods.
