Navigational Error Reduction of Underwater Vehicles with Selective Bathymetric SLAM

Stuckey, Roger A.

doi:10.3182/20120410-3-pt-4028.00021

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…Barkby and Williams [23], using the Gaussian process and predicting the map in unknown regions, were able to employ a particle filter without actual overlap between tracks. Stuckey and Roger [24] used pre-defined matching areas in order to generate observations for the EKF. Whenever a vehicle passes such an area, it can update its navigation based on the previous visits.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

AUV Navigation Correction Based on Automated Multibeam Tile Matching

Mohrmann

Greinert

2022

Sensors

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“…Palomer et al [13] use the probabilistic iterative closest point algorithm for 3D underwater SLAM to obtain consistent maps, but only the relative positions of the patches are corrected, the internal patch error cannot be modified, and this method cannot be used online due to the high computational cost. Stuckey [14] achieves BSLAM using navigation cells, which are patches stored as grid submaps along the AUV trajectory. When the AUV passes through a previously explored patch, the new cell is compared to the original one, and the observations are updated accordingly.…”

Section: Related Workmentioning

confidence: 99%

Bathymetric Particle Filter SLAM Based on Mean Trajectory Map Representation

Zhang

Zheng

et al. 2021

IEEE Access

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To obtain independent navigation results for autonomous underwater vehicles (AUVs) and construct high-resolution consistent seabed maps, a particle filter-based bathymetric simultaneous localization and mapping (BSLAM) method with the mean trajectory map representation is proposed. To reduce the computational consumption, particles only keep the current estimated position of the vehicle, while all historical states of the vehicle are stored in the mean trajectory map. Using this set-up, only the weights of the particles which closed to the mean trajectory map are calculated with newly collected bathymetric data. A hierarchical clustering procedure is also discussed to identify invalid loop closures. The performance of the proposed method is validated using both the simulated data and the field data collected from sea trails. The results demonstrate that the proposed method is 50% more accurate and 50% faster than a state-ofthe-art particle filter-based BSLAM method, and it has similar accuracy but 30% faster compared with a graph-based BSLAM method.INDEX TERMS Autonomous underwater vehicle, bathymetric simultaneous localization and mapping, particle filter, hierarchical clustering.

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“…Various authors have reported the use of PF for underwater terrain navigation. Examples of this are the works by Bachmann and Williams (2003); Karlsson et al (2003); Williams and Mahon (2006); Nakatani et al (2009); Morice et al (2009); Meduna et al (2010); Murangira et al (2011); Stuckey (2012); Melo and Matos (2013).…”

Section: Bayesian Estimationmentioning

confidence: 99%

“…Later, Kim and Sang (2011) introduced a computationally efficient SLAM algorithm, able to the elevation changes in undulating terrain and simultaneously localize the vehicle's position relative to the map. Stuckey (2012) proposed an EKFbased Selective Bathymetric SLAM algorithm to improve the navigational accuracy of underwater vehicles.…”

Section: Bathymetric Slammentioning

confidence: 99%

Survey on advances on terrain based navigation for autonomous underwater vehicles

2017

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The autonomy of robotic underwater vehicles is dependent on the ability to perform long-term and long-range missions without need of human intervention. While current state-of-the-art underwater navigation techniques are able to provide sufficient levels of precision in positioning, they require the use of support vessels or acoustic beacons. This can pose limitations on the size of the survey area, but also on the whole cost of the operations. Terrain Based Navigation is a sensor-based navigation technique that bounds the error growth of deadreckoning using a map with terrain information, provided that there is enough terrain variability. An obvious advantage of Terrain Based Navigation is the fact that no external aiding signals or devices are required. Because of this unique feature, terrain navigation has the potential to dramatically improve the autonomy of Autonomous Underwater Vehicles (AUVs). This paper consists on a comprehensive survey on the recent developments for Terrain Based Navigation methods proposed for AUVs. The survey includes a brief introduction to the original Terrain Based Navigation formulations, as well as a description of the algorithms, and a list of the different implementation alternatives found in the literature. Additionally, and due to the relevance, Bathymetric SLAM techniques will also be discussed.

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Navigational Error Reduction of Underwater Vehicles with Selective Bathymetric SLAM

Cited by 11 publications

References 13 publications

AUV Navigation Correction Based on Automated Multibeam Tile Matching

AUV Navigation Correction Based on Automated Multibeam Tile Matching

Bathymetric Particle Filter SLAM Based on Mean Trajectory Map Representation

Survey on advances on terrain based navigation for autonomous underwater vehicles

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