FEVO-LOAM: Feature Extraction and Vertical Optimized Lidar Odometry and Mapping

Wang, Zelin; Yang, Limin; Gao, Feng; Wang, Liangyu

doi:10.1109/lra.2022.3201689

IEEE Robot. Autom. Lett.

2022

DOI: 10.1109/lra.2022.3201689

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FEVO-LOAM: Feature Extraction and Vertical Optimized Lidar Odometry and Mapping

Zelin Wang

Limin Yang

Feng Gao

et al.

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Cited by 10 publications

References 28 publications

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ROLO‐SLAM: Rotation‐Optimized LiDAR‐Only SLAM in Uneven Terrain With Ground Vehicle

Wang,

Ren,

Zhang

et al. 2025

Journal of Field Robotics

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LiDAR‐based SLAM is recognized as one effective method to offer localization guidance in rough environments. However, off‐the‐shelf LiDAR‐based SLAM methods suffer from significant pose estimation drifts, particularly components relevant to the vertical direction, when passing to uneven terrains. This deficiency typically leads to a conspicuously distorted global map. In this article, a LiDAR‐based SLAM method is presented to improve the accuracy of pose estimations for ground vehicles in rough terrains, which is termed Rotation‐Optimized LiDAR‐Only (ROLO) SLAM. The method exploits a forward location prediction to coarsely eliminate the location difference of consecutive scans, thereby enabling separate and accurate determination of the location and orientation at the front‐end. Furthermore, we adopt a parallel‐capable spatial voxelization for correspondence‐matching. We develop a spherical alignment‐guided rotation registration within each voxel to estimate the rotation of vehicle. By incorporating geometric alignment, we introduce the motion constraint into the optimization formulation to enhance the rapid and effective estimation of LiDAR's translation. Subsequently, we extract several keyframes to construct the submap and exploit an alignment from the current scan to the submap for precise pose estimation. Meanwhile, a global‐scale factor graph is established to aid in the reduction of cumulative errors. In various scenes, diverse experiments have been conducted to evaluate our method. The results demonstrate that ROLO‐SLAM excels in pose estimation of ground vehicles and outperforms existing state‐of‐the‐art LiDAR SLAM frameworks.

show abstract

ROLO‐SLAM: Rotation‐Optimized LiDAR‐Only SLAM in Uneven Terrain With Ground Vehicle

Wang,

Ren,

Zhang

et al. 2025

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

An efficient ground segmentation approach for LiDAR point cloud utilizing adjacent grids

Dong,

Liu,

Dong

et al. 2024

Machine Vision and Applications

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OR-LIM: Observability-aware robust LiDAR-inertial-mapping under high dynamic sensor motion

Cong,

Chen,

Yang

et al. 2024

ISPRS Journal of Photogrammetry and Remote Sensing

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FEVO-LOAM: Feature Extraction and Vertical Optimized Lidar Odometry and Mapping

Cited by 10 publications

References 28 publications

ROLO‐SLAM: Rotation‐Optimized LiDAR‐Only SLAM in Uneven Terrain With Ground Vehicle

ROLO‐SLAM: Rotation‐Optimized LiDAR‐Only SLAM in Uneven Terrain With Ground Vehicle

An efficient ground segmentation approach for LiDAR point cloud utilizing adjacent grids

OR-LIM: Observability-aware robust LiDAR-inertial-mapping under high dynamic sensor motion

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