Year: 2005
Type: conference-paper
Abstract: This paper introduces a dynamic map for mobile robots that adapts continuously over time. It resolves the stabilityplasticity dilemma (the trade-off between adaptation to new patterns and preservation of old patterns) by representing the environment over multiple timescales simultaneously (5 in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the timescale.Robust statistics are used to interpret the samples.It is shown that this approach can track both stationary and non-stationary elements of the environment, covering the full spectrum of variations from moving objects to structural changes.The method was evaluated in a five week experiment in a real dynamic environment.Experimental results show that the resulting map is stable, improves its quality over time and adapts to changes.
Source: Robotics: Science and Systems
Authors: Peter Biber, Tom Duckett
Institutions: University of Tübingen, Örebro University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W3143919370