Bathymetric SLAM with no map overlap using Gaussian processes

Barkby,; Williams, Stefan B.; Pizarro, Oscar; Jakuba,

doi:10.1109/iros.2011.6048284

Cited by 2 publications

(2 citation statements)

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“…Barkby et al [9] introduced the BP-Slam algorithm, which is a Rao-Blackwellized particle filter, wherein the map is sampled and the pose of the vehicle is tracked analytically for each particle using an EKF filter. In subsequent papers [10] [3], the authors extended the method with a probabilistic measurement model based on Gaussian processes. The filter keeps a record of all measurements, and constructs a GP for each particle that can then be used for regression and comparison with new measurements.…”

Section: Related Workmentioning

confidence: 99%

“…Historically, elevation grids [9] and point clouds [7] [1] have been the main map representation in bathymetric SLAM. As we have seen, more recently [6] and to some extent [10][3] have instead proposed use Gaussian processes to represent the sea floor elevation. However, there are also other, more exotic representations such as the signed distance function [12].…”

Section: Related Workmentioning

confidence: 99%

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Sparse Gaussian Process SLAM, Storage and Filtering for AUV Multibeam Bathymetry

Bore¹,

Torroba²,

Folkesson³

2018

2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)

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With dead-reckoning from velocity sensors, AUVs may construct short-term, local bathymetry maps of the sea floor using multibeam sensors. However, the position estimate from dead-reckoning will include some drift that grows with time. In this work, we focus on longterm onboard storage of these local bathymetry maps, and the alignment of maps with respect to each other. We propose using Sparse Gaussian Processes for this purpose, and show that the representation has several advantages, including an intuitive alignment optimization, data compression, and sensor noise filtering. We demonstrate these three key capabilities on two real-world datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sparse Gaussian Process SLAM, Storage and Filtering for AUV Multibeam Bathymetry

Bore¹,

Torroba²,

Folkesson³

2018

2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)

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AUV Navigation Correction Based on Automated Multibeam Tile Matching

Mohrmann

Greinert

2022

Sensors

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Ocean science and hydroacoustic seafloor mapping rely on accurate navigation underwater. By exploiting terrain information provided by a multibeam echosounder system, it is possible to significantly improve map quality. This article presents an algorithm capable of improving map quality and accuracy by aligning consecutive pings to tiles that are matched pairwise. A globally consistent solution is calculated from these matches. The proposed method has the potential to be used online in addition to other navigation solutions, but is mainly targeted for post processing. The algorithm was tested using different parameter settings on an AUV and a ship-based dataset. The ship-based dataset is publicly available as a benchmark. The original accurate navigation serving as a ground truth, alongside trajectories that include an artificial drift, are available. This allows quantitative comparisons between algorithms and parameter settings.

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Bathymetric SLAM with no map overlap using Gaussian processes

Cited by 2 publications

References 0 publications

Sparse Gaussian Process SLAM, Storage and Filtering for AUV Multibeam Bathymetry

Sparse Gaussian Process SLAM, Storage and Filtering for AUV Multibeam Bathymetry

AUV Navigation Correction Based on Automated Multibeam Tile Matching

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