.png){kind=icon}
返回
Matching horizon features using a Messy Genetic Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/S0045-7825(99)00399-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Three line segment matching techniques are compared on horizon line matching problems. The application is to reduce camera angle uncertainty for outdoor robots. Each of the three algorithms seeks an optimal many-to-many match between a subset of 2D line segments extracted fi om a rendered terrain map and segments extracted from an image. Two algorithms are non-deterministic, with one performing Random Starts Local Search and the other using a Messy Genetic Algorithm. The third algorithm searches a list of ranked key feature matches using a limited deterministic local search. The Random Starts Local Search Algorithm is the slowest of the three algorithms and fails to iind the best match on 9 out of 54 problems. In contrast, the Messy Genetic Algorithm correctly solves all 54 test problems. The Key Feature Algorithm is the fastest, but it fails to find the optimal match in three cases. The results suggest the Messy Genetic Algorithm is superior to the other two algorithms on problems of this type. The test imagery was collected on one of the Unmanned Ground Vehicle Program's vehicles operating at the Lockheed Martin Demo C test site. Using the optimal matches it is shown that uncertainty in camera pointing angle is reduced from several degrees to less than a degree. (C) 2000 Elsevier Science S.A. All rights reserved.
Keyword:
Messy Genetic Algorithm
line segment matching
robot orientation correction
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:
7.3
论文数:
1.3W
被引数:
5.6W
机构
暂无机构信息
引用论文
Multiple mechanisms of resistance to cisplatin toxicity in an Escherichia coli K12 mutant.png)
Toxicology
ToxicologyIF0
PREAI
分享收藏
分享收藏
查看更多内容