.png){kind=icon}
返回
Challenging data sets for point cloud registration algorithms
.png){kind=icon}
OA
AI
DOI：10.1177/0278364912458814.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The number of registration solutions in the literature has bloomed recently. The iterative closest point, for example, could be considered as the backbone of many laser-based localization and mapping systems. Although they are widely used, it is a common challenge to compare registration solutions on a fair base. The main limitation is to overcome the lack of accurate ground truth in current data sets, which usually cover environments only over a small range of organization levels. In computer vision, the Stanford 3D Scanning Repository pushed forward point cloud registration algorithms and object modeling fields by providing high-quality scanned objects with precise localization. We aim to provide similar high-caliber working material to the robotic and computer vision communities but with sceneries instead of objects. We propose eight point cloud sequences acquired in locations covering the environment diversity that modern robots are susceptible to encounter, ranging from inside an apartment to a woodland area. The core of the data sets consists of 3D laser point clouds for which supporting data (Gravity, Magnetic North and GPS) are given for each pose. A special effort has been made to ensure global positioning of the scanner within mm-range precision, independent of environmental conditions. This will allow for the development of improved registration algorithms when mapping challenging environments, such as those found in real-world situations.(1)
Keyword:
Field robots
field and service robotics
search and rescue robots
range sensing
sensing and perception
computer vision
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5
论文数:
2.4K
被引数:
1.5W
机构
引用论文
A Facile Synthesis and Photoluminescent Properties of Redispersible CeF3, CeF3:Tb3+, and CeF3:Tb3+/LaF3 (Core/Shell) Nanoparticles..png)
ChemInform
ChemInformIF0
PREAI
分享收藏分享收藏The Marulan Data Sets: Multi-sensor Perception in a Natural Environment with Challenging ConditionsMarulan数据集: 具有挑战性条件的自然环境中的多传感器感知OAAI
OAAI分享收藏 没有更多内容
没有更多内容