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.

Additional Metadata
Conference 27th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2014
Citation
Nematallah, H. (H.), Lui, S. (S.), Atia, M, Givigi, S. (S.), & Noureldin, A. (A.). (2014). A fast LiDAR-based features extraction/tracking using hough transforms and fuzzy C-means clustering for LiDAR-aided Multisensor Navigation Systems. In 27th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2014 (pp. 3184–3193).