Spectral analysis for long-term robotic mapping

Krajnik, Tomas, Pulido Fentanes, Jaime, Cielniak, Grzegorz , Dondrup, Christian and Duckett, Tom (2014) Spectral analysis for long-term robotic mapping. In: 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), May 31 - June 7, 2014, Hong Kong, China.

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Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive

Abstract

This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of ‘memory decay’. While these models keep up with slowly changing environments, their utilization in dynamic, real world
environments is difficult.

The representation proposed in this paper models the environment’s spatio-temporal dynamics by its frequency spectrum. The spectral representation of the time domain allows to identify, analyse and remember regularly occurring environment processes in a computationally efficient way. Knowledge of the periodicity of the different environment processes constitutes the model predictive capabilities, which are especially useful for long-term mobile robotics scenarios.

In the experiments presented, the proposed approach is applied to data collected by a mobile robot patrolling an indoor
environment over a period of one week. Three scenarios are investigated, including intruder detection and 4D mapping. The results indicate that the proposed method allows to represent arbitrary timescales with constant (and low) memory requirements, achieving compression rates up to 106 . Moreover, the representation allows for prediction of future environment’s state with ∼ 90% precision.

Keywords:long-term autonomy, mobile robotics, spa- tiotemporal mapping, Mapping
Subjects:H Engineering > H670 Robotics and Cybernetics
H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:13273
Deposited On:05 Feb 2014 12:25

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