A Comparison of Submap Registration Methods for Multibeam Bathymetric Mapping

Torroba, Ignacio; Bore, Nils; Folkesson, John

doi:10.1109/auv.2018.8729731

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…A version of the generalized iterative closest point (GICP) from [19] restricted to x,y and yaw is applied to find the relative transformation T GICP ∈ R 3 between the submap frames that minimizes the plane-to-plane distance between the point clouds. GICP is more suitable for the registration of large, mostly flat submaps [20] than the variants of ICP typically used on other more structured underwater environments ( [11], [18], [17]). This is because GICP models locally the surface from both point clouds during the matching step, easing the registration of large planar surfaces with minimal overlap and scarce features.…”

Section: Slam With Bathymetric Submapsmentioning

confidence: 99%

Towards Autonomous Industrial-Scale Bathymetric Surveying

Torroba

Bore

Folkesson

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Both higher efficiency and cost reduction can be gained from automating bathymetric surveying for offshore applications such as pipeline, telecommunication or power cables installation and inspection on the seabed. We present a SLAM system that optimizes the geo-referencing of bathymetry surveys by fusing the dead-reckoning sensor data from the surveying vehicle with constraints from the maximization of the geometric consistency of overlapping regions of the survey.The framework has been extensively tested on bathymetric maps from both simulation and several actual industrial surveys and has proved robustness over different types of terrain. We demonstrate that our system is able to maximize the consistency of the final map even when there are large sections of the survey with reduced topographic variation. The framework has been made publicly available together with the simulation environment used to test it and some of the datasets.

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Section: Slam With Bathymetric Submapsmentioning

confidence: 99%

Towards Autonomous Industrial-Scale Bathymetric Surveying

Torroba

Bore

Folkesson

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…In the remainder of this paper we apply the presented general approach for learning Gaussian distributions from unordered sets of points to the specific problem of bathymetric graph SLAM with GICP registration as introduced in [21]. The reason to focus on GICP as opposed to ICP is that this method works better on the kind of bathymetric point clouds produced from surveys of unstructured seabed, as discussed in [6].…”

Section: Methodsmentioning

confidence: 99%

“…Of the various types of sonar, multibeam echo sounders (MBES) provide the most suitable type of raw data for SLAM methods. This data is essentially a point cloud sampled from the bathymetric surface, and applying registration methods to these measurements is a well-studied problem [6]. However, when fusing the output of the registration into the Bayesian estimate of the AUV state, the uncertainty of the transform must be modelled, since it represents the weight of the measurement.…”

Section: Introductionmentioning

confidence: 99%

PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM

Torroba

Sprague

Bore

et al. 2020

IEEE Robot. Autom. Lett.

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Registration methods for point clouds have become a key component of many SLAM systems on autonomous vehicles. However, an accurate estimate of the uncertainty of such registration is a key requirement to a consistent fusion of this kind of measurements in a SLAM filter. This estimate, which is normally given as a covariance in the transformation computed between point cloud reference frames, has been modelled following different approaches, among which the most accurate is considered to be the Monte Carlo method. However, a Monte Carlo approximation is cumbersome to use inside a time-critical application such as online SLAM. Efforts have been made to estimate this covariance via machine learning using carefully designed features to abstract the raw point clouds [1]. However, the performance of this approach is sensitive to the features chosen. We argue that it is possible to learn the features along with the covariance by working with the raw data and thus we propose a new approach based on PointNet [2]. In this work, we train this network using the KL divergence between the learned uncertainty distribution and one computed by the Monte Carlo method as the loss. We test the performance of the general model presented applying it to our target use-case of SLAM with an autonomous underwater vehicle (AUV) restricted to the 2-dimensional registration of 3D bathymetric point clouds.

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“…From left to right: a submap is fed into PointNetKL to produce a global feature vector that is then fed into a multilayer perceptron to produce the parameters of a Cholesky decomposition and construct a positive-definite covariance matrix. and applying registration methods to these measurements is a well-studied problem [6]. However, when fusing the output of the registration into the Bayesian estimate of the AUV state, the uncertainty of the transform must be modelled, since it represents the weight of the measurement.…”

Section: Introductionmentioning

confidence: 99%

PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM

Torroba,

Sprague,

Bore

et al. 2020

Preprint

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A Comparison of Submap Registration Methods for Multibeam Bathymetric Mapping

Cited by 7 publications

References 23 publications

Towards Autonomous Industrial-Scale Bathymetric Surveying

Towards Autonomous Industrial-Scale Bathymetric Surveying

PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM

PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM

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