  One of the main challenges in simultaneous localization and mapping (SLAM) is
real-time processing. High-computational loads linked to data acquisition and
processing complicate this task. This article presents an efficient feature
extraction approach for mapping structured environments. The proposed
methodology, weighted conformal LiDAR-mapping (WCLM), is based on the
extraction of polygonal profiles and propagation of uncertainties from raw
measurement data. This is achieved using conformal M bius transformation. The
algorithm has been validated experimentally using 2-D data obtained from a
low-cost Light Detection and Ranging (LiDAR) range finder. The results obtained
suggest that computational efficiency is significantly improved with reference
to other state-of-the-art SLAM approaches.
