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Lmapping: tightly-coupled LiDAR-inertial odometry and mapping for degraded environments

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Abstract

Simultaneous localization and mapping (SLAM) is a critical technology in the field of robotics. Over the past decades, numerous SLAM algorithms based on 2D LiDAR have been proposed. In general, these algorithms achieve good results in indoor environments. However, for geometrically degenerated environments such as long hallways, robust localization of robots remains a challenging problem. In this study, we focus on the challenges faced by LiDAR SLAM in such conditions. We propose 2D LiDAR SLAM algorithm Lmapping that employs an IMU-centric data-processing pipeline. In the front-end, a point cloud is directly registered to a probabilistic map; environment recognition is accomplished using a new method that relies on LiDAR measurements. And this method is more suitable for front-end matching based on grid maps. LiDAR odometry and IMU pre-integration are then integrated to build a local factor graph in the sliding window of the submap. When the environment is degraded, an Error-State Kalman Filter (ESKF) is added as a constraint to correct the IMU bias. In the back-end, through mutual matching within and between submaps, and loop detection, accumulated errors from the front-end are reduced. To improve flexibility for different sensor combinations, Lmapping supports multiple LiDAR inputs and facilitates initialization with a common six-axis IMU. Extensive experiments have shown that Lmapping greatly outperforms the current mainstream 2D-SLAM algorithm (Cartographer) in terms of the mapping effect and localization accuracy, with high efficiency in degraded environments.

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Notes

  1. When CPU usage exceeds 100%, it is usually because multiple CPU cores are executing tasks simultaneously. For example, on a CPU with four physical cores, if the usage of all cores reaches 100%, the total CPU usage will be displayed as 400%.

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Funding

The presented work was supported by the National Key R&D Programmes of China with 2022YFE0117100, Basic Industrial Science and Technology Project of Wenzhou with G20210006, Key Scientific and Technological Innovation Research Project of Wenzhou with ZG2020029.

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China

    Jingliang Zou, Liang Shao, Heshen Tang, Huangsong Chen, Haoran Bao & Xiaoming Pan

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  1. Jingliang Zou
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  2. Liang Shao
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  4. Huangsong Chen
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Correspondence to Liang Shao.

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Zou, J., Shao, L., Tang, H. et al. Lmapping: tightly-coupled LiDAR-inertial odometry and mapping for degraded environments. Intel Serv Robotics 16, 583–597 (2023). https://doi.org/10.1007/s11370-023-00482-6

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  • DOI: https://doi.org/10.1007/s11370-023-00482-6

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