ImPosing: 効率的な視覚的自己位置推定のための暗黙的ポーズ符号化
ImPosing: Implicit Pose Encoding for Efficient Visual Localization
画像とカメラポーズを別々のニューラルネットで共通の潜在表現に埋め込み、類似度を階層的に評価することで、都市規模の環境でリアルタイムに車両の位置・姿勢を推定する手法を提案。
著者: Arthur Moreau, Thomas Gilles, Nathan Piasco, Dzmitry Tsishkou, Bogdan Stanciulescu, Arnaud de La Fortelle
分類: cs.CV, cs.RO
原文アブストラクト
We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments. Visual localization algorithms determine the position and orientation from which an image has been captured, using a set of geo-referenced images or a 3D scene representation. Our new localization paradigm, named Implicit Pose Encoding (ImPosing), embeds images and camera poses into a common latent representation with 2 separate neural networks, such that we can compute a similarity score for each image-pose pair. By evaluating candidates through the latent space in a hierarchical manner, the camera position and orientation are not directly regressed but incrementally refined. Very large environments force competitors to store gigabytes of map data, whereas our method is very compact independently of the reference database size. In this paper, we describe how to effectively optimize our learned modules, how to combine them to achieve real-time localization, and demonstrate results on diverse large scale scenarios that significantly outperform prior work in accuracy and computational efficiency.
