Self-localization in non-stationary environments using omni-directional vision

被引：28

作者：

Andreasson, Henrik ^{[1
]}

Treptow, Andre

Duckett, Tom

机构：

[1] Univ Orebro, Ctr Appl Autonomous Sensor Syst, Dept Technol, S-70182 Orebro, Sweden

[2] Univ Tubingen, Dept Comp Sci, Tubingen, Germany

来源：

ROBOTICS AND AUTONOMOUS SYSTEMS | 2007年 / 55卷 / 07期

关键词：

localization omni-directional vision; Monte Carlo; local features;

D O I：

10.1016/j.robot.2007.02.002

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an image-based approach for localization in non-static environments using local feature descriptors, and its experimental evaluation in a large, dynamic, populated environment where the time interval between the collected data sets is up to two months. By using local features together with panoramic images, robustness and invariance to large changes in the environment can be handled. Results from global place recognition with no evidence accumulation and a Monte Carlo localization method are shown. To test the approach even further, experiments were conducted with up to 90% virtual occlusion in addition to the dynamic changes in the environment. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：541 / 551

页数：11

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