A Fast LiDAR-based Features Extraction/Tracking Using Hough Transforms and Fuzzy C-means Clustering for LiDAR-aided Multisensor Navigation Systems

H. Nematallah, S. Liu, M.M. Atia, S. Givigi, A. Noureldin

Abstract:	This paper proposes a fast feature extraction/tracking methodology for LiDAR-Aided multisensor integrated navigation systems. Hough Transform is applied on the LiDAR range/bearing information in 2D space to detect lines. To filter out noisy observations and outliers and focus only on strong line patterns, a fuzzy C-mean clustering algorithm is utilized. By tracking extracted lines features, the relative 2D orientation/translation motions are estimated. The proposed methodology was applied on an unmanned ground vehicle (UGV) to estimate its 2D relative orientation/translational motion. The estimated LiDAR-based relative orientation/translational changes are fused with Inertial/Odometer measurements by an Extended Kalman Filter (EKF). The integrated solution was compared with Inertial/Odometer standalone navigation output and results showed significant improved accuracy when LiDAR updates are applied.
Published in:	Proceedings of the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2014) September 8 - 12, 2014 Tampa Convention Center Tampa, Florida
Pages:	3184 - 3193
Cite this article:	Nematallah, H., Liu, S., Atia, M.M., Givigi, S., Noureldin, A., "A Fast LiDAR-based Features Extraction/Tracking Using Hough Transforms and Fuzzy C-means Clustering for LiDAR-aided Multisensor Navigation Systems," Proceedings of the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2014), Tampa, Florida, September 2014, pp. 3184-3193.
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In