Tightly Coupled LiDAR-Inertial Odometry and Mapping for Underground Environments

The demand for autonomous exploration and mapping of underground environments has significantly increased in recent years. However, accurately localizing and mapping robots in subterranean settings presents notable challenges. This paper presents a tightly coupled LiDAR-Inertial odometry system that...

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Published inSensors (Basel, Switzerland) Vol. 23; no. 15; p. 6834
Main Authors Chen, Jianhong, Wang, Hongwei, Yang, Shan
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 31.07.2023
MDPI
Subjects
Geospatial data
IMU pre-integration
LiDAR-Inertial odometry
Localization
Mapping
NanoGICP
Optical radar
Optimization
Remote sensing
Robots
Sensors
underground environments
Vehicles
Iran
IMU pre-integration
NanoGICP
underground environments
LiDAR-Inertial odometry
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Summary:The demand for autonomous exploration and mapping of underground environments has significantly increased in recent years. However, accurately localizing and mapping robots in subterranean settings presents notable challenges. This paper presents a tightly coupled LiDAR-Inertial odometry system that combines the NanoGICP point cloud registration method with IMU pre-integration using incremental smoothing and mapping. Specifically, a point cloud affected by dust particles is first filtered out and separated into ground and non-ground point clouds (for ground vehicles). To maintain accuracy in environments with spatial variations, an adaptive voxel filter is employed, which reduces computation time while preserving accuracy. The estimated motion derived from IMU pre-integration is utilized to correct point cloud distortion and provide an initial estimation for LiDAR odometry. Subsequently, a scan-to-map point cloud registration is executed using NanoGICP to obtain a more refined pose estimation. The resulting LiDAR odometry is then employed to estimate the bias of the IMU. We comprehensively evaluated our system on established subterranean datasets. These datasets were collected by two separate teams using different platforms during the DARPA Subterranean (SubT) Challenge. The experimental results demonstrate that our system achieved performance enhancements as high as 50-60% in terms of root mean square error (RMSE).
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23156834