屋外3D LiDARスキャンの向き推定付き場所認識
OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios
単一の3D LiDARスキャンからCNNでコンパクトな記述子を抽出し、地図上の近傍候補検索とヨー角推定を同時に行う場所認識手法を提案。トリプレット損失とハードネガティブマイニングで学習し、NCLT・KITTIで従来手法を上回る性能を示した。
著者: Lukas Schaupp, Mathias Bürki, Renaud Dubé, Roland Siegwart, Cesar Cadena
分類: cs.RO
原文アブストラクト
We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods. We employ a triplet loss function for training and use a hard-negative mining strategy to further increase the performance of our descriptor extractor. In an evaluation on the NCLT and KITTI datasets, we demonstrate that our method outperforms related state-of-the-art approaches based on both data-driven and handcrafted data representation in challenging long-term outdoor conditions.
