セマンティックエッジ整合を用いたコンパクト3Dマップによる単眼カメラ定位
Compact 3D Map-Based Monocular Localization Using Semantic Edge Alignment
低コスト単眼カメラとIMUで、都市環境に適応した簡略化3Dマップとセマンティックエッジ特徴の整合によりドリフトのないグローバル定位を実現した研究。
著者: Kejie Qiu, Shenzhou Chen, Jiahui Zhang, Rui Huang, Le Cui, Siyu Zhu, Ping Tan
分類: cs.RO
原文アブストラクト
Accurate localization is fundamental to a variety of applications, such as navigation, robotics, autonomous driving, and Augmented Reality (AR). Different from incremental localization, global localization has no drift caused by error accumulation, which is desired in many application scenarios. In addition to GPS used in the open air, 3D maps are also widely used as alternative global localization references. In this paper, we propose a compact 3D map-based global localization system using a low-cost monocular camera and an IMU (Inertial Measurement Unit). The proposed compact map consists of two types of simplified elements with multiple semantic labels, which is well adaptive to various man-made environments like urban environments. Also, semantic edge features are used for the key image-map registration, which is robust against occlusion and long-term appearance changes in the environments. To further improve the localization performance, the key semantic edge alignment is formulated as an optimization problem based on initial poses predicted by an independent VIO (Visual-Inertial Odometry) module. The localization system is realized with modular design in real time. We evaluate the localization accuracy through real-world experimental results compared with ground truth, long-term localization performance is also demonstrated.
