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DV-LIO: LiDAR-inertial Odometry based on dynamic merging and smoothing voxel

Published online by Cambridge University Press:  02 April 2025

Chenyu Shen Open the ORCID record for Chenyu Shen [Opens in a new window] ,
Wanbiao Lin ,
Siyang Sun ,
Wenlan Ouyang ,
Bohan Shi  and
Lei Sun
Chenyu Shen
Affiliation:
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China
Wanbiao Lin
Affiliation:
Shenzhen research institute of Nankai University, Shenzhen, China
Siyang Sun
Affiliation:
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China
Wenlan Ouyang
Affiliation:
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China
Bohan Shi
Affiliation:
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China
Lei Sun*
Affiliation:
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China
*
Corresponding author: Lei Sun; Email: [email protected]
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Abstract

This paper proposes a LiDAR-inertial odometry (LIO) based on the dynamic voxel merging and smoothing method, DV-LIO. In this approach, a local map management mechanism based on feature distribution is introduced to unify the features of similar adjacent voxels through dynamic merging and segmentation, thereby improving the perceptual consistency of environmental features. Moreover, a novel noise detector that performs noise detection and incremental filtering by evaluating the consistency of voxel features is designed to further reduce local map noise and improve mapping accuracy while ensuring real-time algorithm performance. Meanwhile, to ensure the computational efficiency of the LIO system, a point cache is set for each voxel, which allows the voxel to be updated incrementally and intermittently. The proposed method is extensively evaluated on datasets gathered over various environments, including campus, park, and unstructured gardens.

Keywords

SLAMLiDAR-inertial odometryrobot localizationmappingmeasurement uncertainty
Type
Research Article
Information
Robotica , First View , pp. 1 - 13
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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