Informative Path Planning for Active Field Mapping under Localization Uncertainty
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust, high-quality maps. To address this issue, we introduce an informative planning framework for active mapping that explicitly accounts for the pose uncertainty in both the mapping and planning tasks. Our strategy exploits a Gaussian Process (GP) model to capture a target environmental field given the uncertainty on its inputs. For planning, we formulate a new utility function that couples the localization and field mapping objectives in GP-based mapping scenarios in a principled way, without relying on manually-tuned parameters. Extensive simulations show that our approach outperforms existing strategies, reducing mean pose uncertainty and map error. We present a proof of concept in an indoor temperature mapping scenario. © 2020 IEEE.
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published
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Book title
2020 IEEE International Conference on Robotics and Automation (ICRA)
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Pages / Article No.
10751 - 10757
Publisher
IEEE
Event
IEEE International Conference on Robotics and Automation (ICRA 2020)
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Software
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Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.