Spectral Analysis for Long-Term Robotic Mapping

Tomáš Krajník, Jaime Pulido Fentanes, Grzegorz Cielniak, Christian Dondrup, Tom Duckett

Research output: Chapter in Book/Report/Conference proceedingConference contribution

56 Citations (Scopus)


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 states with ~ 90% precision.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Robotics and Automation (ICRA)
Number of pages6
ISBN (Electronic)9781479936854
Publication statusPublished - 29 Sept 2014
Event2014 IEEE International Conference on Robotics and Automation - Hong Kong, China
Duration: 31 May 20147 Jun 2014


Conference2014 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2014
CityHong Kong


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