2024-03-29T15:34:47Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1273402019-05-31T07:36:14Zcom_10261_106com_10261_4col_10261_359
DIGITAL.CSIC
author
Jadidi, Maani Ghaffari
author
Valls Miró, Jaime
author
Valencia, Rafael
author
Andrade-Cetto, Juan
funder
Ministerio de Ciencia e Innovación (España)
funder
University of Technology Sydney
2016-01-07T12:25:07Z
2016-01-07T12:25:07Z
2014
IEEE International Conference on Robotics and Automation: 6077-6082 (2014)
http://hdl.handle.net/10261/127340
10.1109/ICRA.2014.6907754
http://dx.doi.org/10.13039/501100004837http://dx.doi.org/10.13039/501100001775
An information-driven autonomous robotic explo- ration method on a continuous representation of unknown envi- ronments is proposed in this paper. The approach conveniently handles sparse sensor measurements to build a continuous model of the environment that exploits structural dependencies without the need to resort to a fixed resolution grid map. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian process providing frontier boundaries for further exploration. The resulting continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic stop criterion for a desired sensitivity. The performance of the proposed approach is evaluated through simulation results in the well-known Freiburg and Cave maps.
eng
openAccess
Exploration on continuous Gaussian process frontier maps
artículo
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