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Autonomous Robots

, Volume 42, Issue 6, pp 1133–1150 | Cite as

Long-term online multi-session graph-based SPLAM with memory management

  • Mathieu Labbé
  • François Michaud
Article

Abstract

For long-term simultaneous planning, localization and mapping (SPLAM), a robot should be able to continuously update its map according to the dynamic changes of the environment and the new areas explored. With limited onboard computation capabilities, a robot should also be able to limit the size of the map used for online localization and mapping. This paper addresses these challenges using a memory management mechanism, which identifies locations that should remain in a Working Memory (WM) for online processing from locations that should be transferred to a Long-Term Memory (LTM). When revisiting previously mapped areas that are in LTM, the mechanism can retrieve these locations and place them back in WM for online SPLAM. The approach is tested on a robot equipped with a short-range laser rangefinder and a RGB-D camera, patrolling autonomously 10.5 km in an indoor environment over 11 sessions while having encountered 139 people.

Keywords

SLAM Path planning Pose graph Multi-session Loop closure detection 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Interdisciplinary Institute for Technological Innovation (3IT)Université de SherbrookeSherbrookeCanada

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