Hybrid metric-feature mapping based on camera and Lidar sensor fusion. (15th February 2023)

• Record Type: Journal Article
• Authors: Liu, Chao; Zhang, Guojun; Rong, Youmin; Shao, Wenjun; Meng, Jie; Li, Gen; Huang, Yu
• Is Part Of: Measurement. Volume 207(2023)
• Publication Date: 2023-02-15
• Subjects: Fusion odometry -- Hybrid map -- Scan matching -- Loop closure -- Multi-layer optimization; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.112411
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A novel strategy to fuse the camera and 2D-Lidar information for motion estimation. A new strategy that utilizes both visual and Lidar data to determine the loop candidate. Can obtain a hybrid metric-feature submap. Can generate a global consistent metric-feature scene representation. Experiments verify the mapping method is accurate and robust. Abstract: Mapping the surroundings is a challenging task required for mobile robot. Fusion of heterogeneous sensors measurement has gradually been the main focus of robot perception. In this work, the Lidar and camera measurement is fused to obtain robust motion estimation and construct hybrid metric-feature map. By coupling visual information, the powerful tracking ability of visual features is heuristically applied to support the scan matching. With the estimation of Lidar odometry, the camera motion in complicated scene can be guaranteed. Meanwhile, the hybrid map is divided into submaps to improve the mapping efficiency, and each submap is composed of a grid-based metric submap and a group of visual features. Then a multi-stage loop closure strategy is applied to determine the loop candidate. Besides, multi-layer optimization including local bundle adjustment and pose graph optimization is performed to obtain global consistent map. The effectiveness of the proposed method is verified in different scenarios.