EKF-SLAM for AUV navigation under probabilistic sonar scan-matching

Mallios, Angelos; Ridao, Pere; Ribas, David; Maurelli, Francesco; Pétillot, Yvan

doi:10.1109/iros.2010.5649246

Cited by 55 publications

(42 citation statements)

References 23 publications

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“…As proposed in [24], the data association is achieved by choosing those scans whose corresponding poses fall in the neighbouring range of the pose of current scans as the loop closing candidates. We refer this strategy as pose-threshold based association.…”

Section: B Scan Generationmentioning

confidence: 99%

“…There are three types of underwater sonar sensors that have been deployed in underwater SLAM applications, namely side-scan sonar [20], [21], multi-beam sonar [22], [23], MSS [9], [11], [24]. For a side-scan sonar, the inertial sensors such as DVL and IMU are firstly used to provide navigation data relative to the vehicle reference frame, such as velocities, orientations and depth.…”

Section: Introductionmentioning

confidence: 99%

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Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

Chen

Wang

et al. 2015

IEEE Sensors J.

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Abstract-This paper proposes a novel localization algorithm for an autonomous underwater vehicle equipped with a mechanical scanning sonar that has a slow frequency of data sampling. The proposed approach incrementally constructs a pose graph and conducts graph optimization to correct the robot poses. The construction of a pose graph has three stages: 1) scan generation which incorporates an extended Kalman filter-based dead reckoning algorithm that takes the robot motion into account while eliminating the sonar scan distortion caused by the motion; 2) data association which is based on Mahanalobis distance and shape matching for determining loop closures; and 3) scan matching which calculates constraints constructs pose graph. The constructed pose graph is then fed into a graph optimizer to find the optimal poses corresponding to each scan. A trajectory correction module uses these optimized poses to correct intermediate poses during the process of scan generation. Both simulation and practical experiments are conducted to verify the viability and accuracy of the proposed algorithm.

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Section: B Scan Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

Chen

Wang

et al. 2015

IEEE Sensors J.

View full text Add to dashboard Cite

show abstract

“…As proposed in [5], the data association is achieved by choosing those scans whose corresponding poses fall in the neighboring range of the pose of current scans as the loop closing candidates. We refer this strategy as pose-threshold based association.…”

Section: W Imentioning

confidence: 99%

“…On the other hand, sonar becomes the most widely used perception sensor for AUVs. There are three types of underwater sonar sensors that have been deployed in underwater SLAM applications, namely side-scan sonar [1], [2], multi-beam sonar [3], [4], Mechanical Scanning Sonar (MSS) [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Pose-based GraphSLAM algorithm for robotic fish with a mechanical scanning sonar

Chen

Wang

2013

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Abstract-This paper proposes a pose-based GraphSLAM algorithm for robotic fish equipped with a Mechanical Scanning Sonar (MSS) that has a low frequency of range readings. The main contribution of this paper is the construction of a pose graph as the front-end part of the normal GraphSLAM algorithm. The proposed algorithm has three stages as follows: 1) scan generation which incorporates a novel Extended Kalman Filter (EKF) based algorithm that takes the fish motion into account; 2) data association which is based on Mahanalobis distance and shape matching for determining loop closures; 3) scan matching which is for constraints calculation and pose graph construction. The constructed pose graph is then fed into a back-end optimizer -g2o for finding the optimal position of robotic fish. The viability and the accuracy of the proposed algorithm are verified by extensive simulations, compared with the dead reckoning and scan matching approaches.

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“…In the following decades, other paradigms have arisen for solving the SLAM problem such as those based on optimization [2], [3], or appearance matching [4], [5], however SLAM through Bayesian filtering remains an active line of research [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

SLAM With Dynamic Targets via Single-Cluster PHD Filtering

Lee

Clark

Salví

2013

IEEE J. Sel. Top. Signal Process.

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Abstract-This paper presents the first algorithm for simultaneous localization and mapping (SLAM) that can estimate the locations of both dynamic and static features in addition to the vehicle trajectory. We model the feature-based SLAM problem as a singlecluster process, where the vehicle motion defines the parent, and the map features define the daughter. Based on this assumption, we obtain tractable formulae that define a Bayesian filter recursion. The novelty in this filter is that it provides a robust multi-object likelihood which is easily understood in the context of our starting assumptions. We present a particle/Gaussian mixture implementation of the filter, taking into consideration the challenges that SLAM presents over target tracking with stationary sensors, such as changing fields of view and a mixture of static and dynamic map features. Monte Carlo simulation results are given which demonstrate the filter's effectiveness with high measurement clutter and non-linear vehicle motion.

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EKF-SLAM for AUV navigation under probabilistic sonar scan-matching

Cited by 55 publications

References 23 publications

Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

Pose-based GraphSLAM algorithm for robotic fish with a mechanical scanning sonar

SLAM With Dynamic Targets via Single-Cluster PHD Filtering

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