Odometry and Mapping for Complex Environment Perception Under Partial-View Sensing

Xinye Dai

doi:10.3390/rs18172959

Odometry and Mapping for Complex Environment Perception Under Partial-View Sensing

2026-09-03

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DOI：10.3390/rs18172959

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Dense partial-view LiDAR observations are attractive for outdoor perception, but limited overlap and viewpoint sensitivity make odometry and mapping less reliable than with spinning LiDARs. Many recent algorithms for this sensing regime are built as LiDAR-inertial odometry frameworks, whose localization and mapping performance can degrade or fail when the IMU state estimation becomes unstable. This paper presents a LiDAR-only framework for complex outdoor scenes using a factor-graph back-end. After denoising and motion compensation, the point cloud is projected onto a range image for ground, planar, edge, and line extraction. Pose estimation is strengthened by degeneracy-aware feature selection, while loop closing combines scan-based and path-based cues to handle partial-view revisits. Experiments in tunnels, urban roads, residential areas, and other challenging scenes show reduced drift and improved mapping consistency for dense limited-FoV LiDAR data.

Keywords:

LiDAR SLAM

odometry and mapping

partial-view sensing

limited-FoV LiDAR

complex environments

mobile mapping

Odometry and Mapping for Complex Environment Perception Under Partial-View Sensing

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