激光雷达
同时定位和映射
计算机科学
稳健性(进化)
人工智能
计算机视觉
离群值
特征提取
加权
惯性测量装置
遥感
机器人
地理
移动机器人
生物化学
化学
基因
医学
放射科
作者
LI Hai-song,Bailing Tian,Hongming Shen,Junjie Lu
标识
DOI:10.1109/tim.2022.3190060
摘要
With development of LiDAR technology, solid-state LiDARs receive a lot of attention for their high reliability, low cost and light weight. However, compared with traditional rotating LiDARs, these solid-state LiDARs pose new challenges on simultaneous localization and mapping (SLAM) due to their small field of view (FoV) in horizontal direction and irregular scanning pattern, which arises the issue of degeneracy in indoor environments. To this end, we propose an accurate, robust and real-time LiDAR-inertial SLAM method for solid-state LiDARs. Firstly, a novel feature extraction based on geometry and intensity is proposed, which is the core of handling with degeneracy. To make full use of extracted features, two multi-weighting functions are designed for planar and edge points respectively in the process of pose optimization. Lastly, a map management module using an image processing method is developed not only to keep time efficiency and space efficiency but also to reduce edge intensity outliers in line map. Qualitative and quantitative evaluations on public and recorded datasets show that the proposed method exhibits similar and even better accuracy with state-of-the-art SLAM methods in wellconstrained scenarios, while only the proposed method can survive in the robustness test towards degenerated indoor lab environment.
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