Automated Object Extraction from MLS Data: A Survey

Chen Kun-yuan, Cheng Ming, Zhou Menglan, Chen Xinqu, Chen Yifei, L. Jonathan, Nie Hongshan
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引用次数: 4

Abstract

Realistic 3D city modeling using Mobile Laser Scanning (MLS) technique experienced a remarkable revolution in aiding urban planning, regulation design, city management, navigation, and emergency responses. This paper focuses on thoroughly examining the advance of automated MLS object extraction techniques over the last five years. Categorized as either on-road or off-road, mainly six objects are included in this paper (road curbs, road markings, pavement cracks, building facades, pole-like objects and trees). MLS extraction techniques applied on typical objects is evaluated according to their method design, degree of automation, precision, and computational efficiency. Recent researches mostly focus on developing accurate object extraction algorithms and most of the reviewed methods can achieve high precision, however, optimizing the trade-off between computational cost and accuracy remains a big challenge. In addition, there is still no general standardized approach to deal with MLS objects extractions to date, most algorithms reviewed in this paper still need some artificial interference to ensure accuracy and efficiency.
从MLS数据中自动提取目标:综述
使用移动激光扫描(MLS)技术的逼真3D城市建模在帮助城市规划、法规设计、城市管理、导航和应急响应方面经历了一场非凡的革命。本文重点对近五年来自动化多目标目标提取技术的进展进行了全面的研究。本文分为道路类和非道路类,主要包括六个对象(道路路缘、道路标线、路面裂缝、建筑立面、杆状物体和树木)。从方法设计、自动化程度、精度和计算效率等方面对典型目标的MLS提取技术进行了评价。近年来的研究主要集中在开发精确的目标提取算法上,大多数方法都能达到较高的精度,但如何优化计算成本和精度之间的权衡仍然是一个很大的挑战。此外,目前还没有通用的标准化方法来处理MLS对象的提取,本文所回顾的大多数算法仍然需要一些人工干扰来保证准确性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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