SIFT,SURF & seasons: Appearance-based long-term localization in outdoor environments |
| |
| Authors: | Christoffer Valgren Achim J Lilienthal |
| |
| Affiliation: | 1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;3. School of Information Engineering, Minzu University of China, Beijing 100081, China;4. Department of Radio-electronic systems, Don State Technical University, Rostov-on-Don 346500, Russia;5. Faculty of Technical Sciences University of Kragujevac, Cacak, Serbia;6. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China;1. Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China;2. School of Computer Science, The University of Nottingham, NG8 1BB, UK;3. School of Computer Science, The University of Nottingham – Ningbo China, 315100, China;4. The College of Electronics and Information Engineering and Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China;5. College of Intelligence Science and Technology, National University of Defense Technology, 410000, China;6. Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China |
| |
| Abstract: | In this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. We investigate a straightforward method that relies on local image features to compare single-image pairs. We first look into which of the dominating image feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then fine-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. The final localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. The final localization rate in the single-image matching, cross-seasonal case is between 80% to 95%. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
