1 research outputs found
Ambient-Aware LiDAR Odometry in Variable 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 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. Firstly, 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