Predictive and adaptive maps for long-term visual navigation in changing environmentsShow others and affiliations
2019 (English)In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Press, 2019, p. 7033-7039Conference paper, Published paper (Refereed)
Abstract [en]
In this paper, we compare different map management techniques for long-term visual navigation in changing environments. In this scenario, the navigation system needs to continuously update and refine its feature map in order to adapt to the environment appearance change. To achieve reliable long-term navigation, the map management techniques have to (i) select features useful for the current navigation task, (ii) remove features that are obsolete, (iii) and add new features from the current camera view to the map. We propose several map management strategies and evaluate their performance with regard to the robot localisation accuracy in long-term teach-and-repeat navigation. Our experiments, performed over three months, indicate that strategies which model cyclic changes of the environment appearance and predict which features are going to be visible at a particular time and location, outperform strategies which do not explicitly model the temporal evolution of the changes.
Place, publisher, year, edition, pages
IEEE Press, 2019. p. 7033-7039
Series
IEEE International Conference on Intelligent Robots and Systems. Proceedings, E-ISSN 2153-0866
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:oru:diva-83845DOI: 10.1109/IROS40897.2019.8967994ISI: 000544658405093Scopus ID: 2-s2.0-85081166809ISBN: 978-1-7281-4004-9 (electronic)OAI: oai:DiVA.org:oru-83845DiVA, id: diva2:1448590
Conference
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 3-8, 2019.
2020-06-292020-06-292023-12-07Bibliographically approved