LocNet：移動体向け3D点群のグローバル自己位置推定
LocNet: Global localization in 3D point clouds for mobile vehicles
LiDAR点群の場所認識を類似度学習で解くSiamese LocNetを提案し、事前地図上での距離情報のみによるグローバル自己位置推定を実現した研究。
著者: Huan Yin, Li Tang, Xiaqing Ding, Yue Wang, Rong Xiong
分類: cs.RO, cs.LG
原文アブストラクト
Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.
