Open access
Date
2016Type
- Conference Paper
Abstract
Although many applications of small Autonomous Surface Vessels rely on two-dimensional state estimation, inspection tasks based on long-range sensors require more accurate attitude estimates. In the context of shoreline monitoring relying on a nodding laser scanner, we evaluate three different extended Kalman filter approaches with respect to an accurate ground truth in the range of millimeters. Our experimental setup allowed us to track the impact of sensors noise, including GPS non-Gaussian error, a phenomenon often underestimated. Extensive field experiments demonstrate that the use of a complementary filter in combination with a model-based extended Kalman filter performed best and reduced velocity errors by 73% compared to GPS. Finally, following our state estimation observations, we present a long-term shore monitoring result highlighting changes in the environment over a period of 6 months. © Springer International Publishing Switzerland 2016. Show more
Permanent link
https://doi.org/10.3929/ethz-a-010170404Publication status
publishedExternal links
Book title
Experimental RoboticsJournal / series
Springer Tracts in Advanced RoboticsVolume
Pages / Article No.
Publisher
SpringerEvent
Subject
AUTONOME ROBOTER; PARAMETERSCHÄTZUNG UND ZUSTANDSSCHÄTZUNG (MATHEMATISCHE STATISTIK); AUTONOMOUS ROBOTS; ESTIMATION OF PARAMETERS AND STATE ESTIMATION (MATHEMATICAL STATISTICS); ASL; State estimation; Autonomous Surface Vessel; ASV; ICP; Shore monitoring; 3D point cloudsOrganisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
Funding
609763 - Long-Term Human-Robot Teaming for Robot-Assisted Disaster Response (EC)
609763 - Long-Term Human-Robot Teaming for Robot-Assisted Disaster Response (EC)
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