画像とLiDARマップの共有埋め込み空間によるグローバル視覚自己位置推定
Global visual localization in LiDAR-maps through shared 2D-3D embedding space
画像とLiDAR点群を共通の埋め込み空間に写像する2D/3D深層学習モデルを学習し、画像から3D LiDARマップ上での場所認識を可能にした研究。
著者: Daniele Cattaneo, Matteo Vaghi, Simone Fontana, Augusto Luis Ballardini, Domenico Giorgio Sorrenti
分類: cs.CV, cs.LG, cs.RO, eess.IV
原文アブストラクト
Global localization is an important and widely studied problem for many robotic applications. Place recognition approaches can be exploited to solve this task, e.g., in the autonomous driving field. While most vision-based approaches match an image w.r.t. an image database, global visual localization within LiDAR-maps remains fairly unexplored, even though the path toward high definition 3D maps, produced mainly from LiDARs, is clear. In this work we leverage Deep Neural Network (DNN) approaches to create a shared embedding space between images and LiDAR-maps, allowing for image to 3D-LiDAR place recognition. We trained a 2D and a 3D DNN that create embeddings, respectively from images and from point clouds, that are close to each other whether they refer to the same place. An extensive experimental activity is presented to assess the effectiveness of the approach w.r.t. different learning paradigms, network architectures, and loss functions. All the evaluations have been performed using the Oxford Robotcar Dataset, which encompasses a wide range of weather and light conditions.
