On the use of likelihood fields to perform sonar scan matching localization

Burguera, Antoni; González, Yolanda; Oliver, Gabriel

doi:10.1007/s10514-009-9108-0

Cited by 44 publications

(16 citation statements)

References 29 publications

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“…so that its meanẐ i t is the roto-translation provided by RANSAC and R i t is the associated covariance. This covariance can either be obtained heuristically from the error computed by Algorithm 1 or using the methods described in References [39,40].…”

Section: Local Loop Detectionmentioning

confidence: 99%

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A Trajectory-Based Approach to Multi-Session Underwater Visual SLAM Using Global Image Signatures

Burguera

Bonin‐Font

2019

JMSE

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This paper presents a multi-session monocular Simultaneous Localization and Mapping (SLAM) approach focused on underwater environments. The system is composed of three main blocks: a visual odometer, a loop detector, and an optimizer. Single session loop closings are found by means of feature matching and Random Sample Consensus (RANSAC) within a search region. Multi-session loop closings are found by comparing hash-based global image signatures. The optimizer refines the trajectories and joins the different maps. Map joining preserves the trajectory structure by adding a single link between the joined sessions, making it possible to aggregate or disaggregate sessions whenever is necessary. All the optimization processes can be delayed until a certain number of loops has been found in order to reduce the computational cost. Experiments conducted in real subsea scenarios show the quality and robustness of this proposal.

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Section: Local Loop Detectionmentioning

confidence: 99%

“…For example, it could be heuristically estimated using the ε A B computed by Algorithm 1. Also, the two methods presented in References [39,40], which rely on a solid theoretical background, can be used.…”

Section: Trajectory Buildingmentioning

confidence: 99%

A Trajectory-Based Approach to Multi-Session Underwater Visual SLAM Using Global Image Signatures

Burguera

Bonin‐Font

2019

JMSE

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“…This process is difficult to implement, due to interference and noises, and requires iteration to refine the final results. For the mathematical property-based scan matching method, it can be a histogram [17], Hough transformation [18], normal distribution transform [19] or cross-correlation [20].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

et al. 2015

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Abstract:The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR) sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman filter (EKF) design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a significant improvement in the vehicle's integrated navigation accuracy. OPEN ACCESSMicromachines 2015, 6 197

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“…The second point cloud is registered to the first using NDT registration, which has been chosen because of its good performance with respect to accuracy and speed, compared to ICP [6,14,15].…”

Section: B 3d Registrationmentioning

confidence: 99%

Improving point-cloud accuracy from a moving platform in field operations

Almqvist

Magnusson

Stoyanov

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data.

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On the use of likelihood fields to perform sonar scan matching localization

Cited by 44 publications

References 29 publications

A Trajectory-Based Approach to Multi-Session Underwater Visual SLAM Using Global Image Signatures

A Trajectory-Based Approach to Multi-Session Underwater Visual SLAM Using Global Image Signatures

Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

Improving point-cloud accuracy from a moving platform in field operations

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