物体レベル意味SLAMのためのロバストなデータ関連付け
Robust Data Association for Object-level Semantic SLAM
幾何制約と物体レベルの意味制約を統合し、ロバストなデータ関連付けと最適化を行う意味SLAMフレームワークを提案。2D Lidar地図と物体検出を組み合わせたコンパクトな地図表現も導入し、公開データセットで精度とロバスト性の向上を示した。
著者: Xueyang Kang, Shunying Yuan
分類: cs.RO, cs.CV
原文アブストラクト
Simultaneous mapping and localization (SLAM) in an real indoor environment is still a challenging task. Traditional SLAM approaches rely heavily on low-level geometric constraints like corners or lines, which may lead to tracking failure in textureless surroundings or cluttered world with dynamic objects. In this paper, a compact semantic SLAM framework is proposed, with utilization of both geometric and object-level semantic constraints jointly, a more consistent mapping result, and more accurate pose estimation can be obtained. Two main contributions are presented int the paper, a) a robust and efficient SLAM data association and optimization framework is proposed, it models both discrete semantic labeling and continuous pose. b) a compact map representation, combining 2D Lidar map with object detection is presented. Experiments on public indoor datasets, TUM-RGBD, ICL-NUIM, and our own collected datasets prove the improving of SLAM robustness and accuracy compared to other popular SLAM systems, meanwhile a map maintenance efficiency can be achieved.
