Adaptive Denoising-Enhanced LiDAR Odometry for Degeneration Resilience in Diverse Terrains

The flexibility of simultaneous localization and mapping (SLAM) algorithms in various environments has consistently been a significant challenge. To address the issue of LiDAR odometry drift in high-noise settings, integrating clustering methods to filter out unstable features has become an effectiv...

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Vydané v:IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 15
Hlavní autori: Ji, Mazeyu, Shi, Wenbo, Cui, Yujie, Liu, Chengju, Chen, Qijun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
3-D LiDAR odometry
Adaptive algorithms
Algorithms
ambient skeleton
Cloud computing
Clustering
Degeneration
degeneration scene detection
diverse terrains
Feature extraction
Heuristic algorithms
Laser radar
Lidar
Odometry
Point cloud compression
point cloud segmentation
Simultaneous localization and mapping
Three-dimensional displays
ISSN:0018-9456, 1557-9662
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Shrnutí:The flexibility of simultaneous localization and mapping (SLAM) algorithms in various environments has consistently been a significant challenge. To address the issue of LiDAR odometry drift in high-noise settings, integrating clustering methods to filter out unstable features has become an effective module of SLAM frameworks. However, reducing the amount of point cloud data can lead to potential loss of information and possible degeneration. As a result, this research proposes a LiDAR odometry that can dynamically assess the point cloud's reliability. The algorithm aims to improve adaptability in diverse settings by selecting important feature points with sensitivity to the level of environmental degeneration. First, a fast adaptive Euclidean clustering algorithm based on range image is proposed, which, combined with depth clustering, extracts the primary structural points of the environment defined as ambient skeleton points. Then, the environmental degeneration level is computed through the dense normal features of the skeleton points, and the point cloud cleaning is dynamically adjusted accordingly. The algorithm is validated on the KITTI benchmark and real environments, demonstrating higher accuracy and robustness in different environments.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3365168