Abstract
Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled in order to move closer to the ultimate goal of life-long autonomy. In computer vision-based methods employed on mobile robots, such as localization or navigation, one of the major issues is the dynamics of the scenes. The autonomous operation of the robot may become unreliable if the changes that are common in dynamic environments are not detected and managed. Moving chairs, opening and closing doors or windows, replacing objects on the desks and other changes make many conventional methods fail. To deal with that, we present a novel method for change detection based on the similarity of local visual features. The core idea of the algorithm is to distinguish important stable regions of the scene from the regions that are changing. To evaluate the change detection algorithm, we have designed a simple visual localization framework based on feature matching and we have performed a series of real-world localization experiments. The results have shown that the change detection method substantially improves the accuracy of the robot localization, compared to using the baseline localization method without change detection.
Original language | English |
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Title of host publication | Proceedings of the European Conference on Mobile Robots (ECMR 2019) |
Editors | Libor Preucil, Sven Behnke, Miroslav Kulich |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Electronic) | 978-1-7281-3605-9 |
DOIs | |
Publication status | Published - 2019 |
Event | ECMR 2019: European Conference on Mobile Robots - Prague, Czech Republic Duration: 4 Sept 2019 → 6 Sept 2019 |
Conference
Conference | ECMR 2019: European Conference on Mobile Robots |
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Country/Territory | Czech Republic |
City | Prague |
Period | 4/09/19 → 6/09/19 |
Bibliographical note
Accepted Author ManuscriptKeywords
- Change detection
- Computer vision in robotics
- Life-long autonomy
- Localization
- Mobile robots
- Place detection