加权共形LiDAR建图用于结构化SLAM
Weighted Conformal LiDAR-Mapping for Structured SLAM
AI总结：
本文提出加权共形LiDAR建图（WCLM），通过共形Möbius变换提取多边形轮廓并传播不确定性，在低成本二维LiDAR数据上验证，显著提升了SLAM的计算效率。
摘要：
同时定位与建图（SLAM）的主要挑战之一是实时处理。与数据采集和处理相关的高计算负载使这一任务变得复杂。本文提出了一种用于结构化环境建图的高效特征提取方法。所提出的方法，即加权共形LiDAR建图（WCLM），基于多边形轮廓的提取以及从原始测量数据中传播不确定性。这是通过共形Möbius变换实现的。该算法已使用从低成本光检测与测距（LiDAR）测距仪获得的二维数据进行了实验验证。结果表明，与其他最先进的SLAM方法相比，计算效率显著提高。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
