Efficient Bathymetric SLAM With Invalid Loop Closure Identification

Ma, Teng; Li, Ye; Zhao, Yuxin; Jiang, Yanqing; Zhang, Qianyi; Pascoal, António

doi:10.1109/tmech.2020.3043136

Cited by 15 publications

(6 citation statements)

References 21 publications

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“…Since the time update includes the system state and control input, the Jacobian matrix should be calculated. Equations (10) and (11) are the Jacobian matrix of the system state and control input, respectively.…”

Section: Time Update Processmentioning

confidence: 99%

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Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar

Guan

Zhou

et al. 2022

JMSE

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Simultaneous localization and mapping (SLAM) is an active localization method for Autonomous Underwater Vehicle (AUV), and it can mainly be used in unknown and complex areas such as coastal water, harbors, and wharfs. This paper presents a practical occupancy grid-based method based on forward-looking sonar for AUV. The algorithm uses an extended Kalman filter (EKF) to estimate the AUV motion states. First, the SLAM method fuses the data coming from the navigation sensors to predict the motion states. Subsequently, a novel particle swarm optimization genetic algorithm (PSO-GA) scan matching method is employed for matching the sonar scan data and grid map, and the matching pose would be used to correct the prediction states. Lastly, the estimated motion states and sonar scan data would be used to update the grid map. The experimental results based on the field data have validated that the proposed SLAM algorithm is adaptable to underwater conditions, and accurate enough to use for ocean engineering practical applications.

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Section: Time Update Processmentioning

confidence: 99%

“…It can obtain the terrain information when the AUV is moving forward. Similarly, SLAM can only be achieved by planning the path of the repeated course [9,10]. Forward-looking sonar transmits sound waves and estimates the front environment information according to the echo intensity.…”

Section: Introductionmentioning

confidence: 99%

Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar

Guan

Zhou

et al. 2022

JMSE

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“…The RANSAC-based method uses RANSAC to estimate a fundamental matrix and discard loop candidates if the number of inliers is less than 12 [7], [15] or 20 [19]. Since a wrong loop closure may cause disastrous failure in building a consistent map [5], the maximal Recall when Precision achieves 100% (R max @1.0) is listed here, as in Table III.…”

Section: Graph Verificationmentioning

confidence: 99%

“…Intelligent robots apply simultaneous localization and mapping (SLAM) [1] technique to incrementally build maps of the environments while estimating their locations. As an indispensable component, loop closure detection (LCD) algorithm is implemented to detect re-observed places and correct accumulated errors during long-term exploration [2]- [5]. Given the low cost of cameras and the rich information provided by images, appearance-based LCD methods [6]- [31] have attracted increasing attention in the last decades.…”

Section: Introductionmentioning

confidence: 99%

Automatic Vocabulary and Graph Verification for Accurate Loop Closure Detection

Yue¹,

Miao²,

Chen³

et al. 2021

Preprint

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Localizing pre-visited places during long-term simultaneous localization and mapping, i.e. loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies. As one of the most effective and efficient solutions, Bag-of-Words (BoW) builds a visual vocabulary to associate features and then detect loops. Most existing approaches that build vocabularies off-line determine scales of the vocabulary by trial-and-error, which often results in unreasonable feature association. Moreover, the accuracy of the algorithm usually declines due to perceptual aliasing, as the BoW-based method ignores the positions of visual features. To overcome these disadvantages, we propose a natural convergence criterion based on the comparison between the radii of nodes and the drifts of feature descriptors, which is then utilized to build the optimal vocabulary automatically. Furthermore, we present a novel topological graph verification method for validating candidate loops so that geometrical positions of the words can be involved with a negligible increase in complexity, which can significantly improve the accuracy of LCD. Experiments on various public datasets and comparisons against several state-of-the-art algorithms verify the performance of our proposed approach.

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“…Autonomous underwater vehicles (AUVs) have become essential tools for human exploration of the ocean, spanning from subsea geological surveys and archaeology to biology [1]. Nevertheless, navigating AUVs remains a challenging task * Authors to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

An effective stereo SLAM with high-level primitives in underwater environment

Xu¹,

Ma²,

Li³

et al. 2023

Meas. Sci. Technol.

Self Cite

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Visual simultaneous localization and mapping (SLAM) algorithms face challenges in complex underwater scenarios, such as turbidity, dynamism, and low texture, where point features are unreliable and can lead to weakened or even failed systems. To overcome these issues, high-level object features are considered due to their accuracy and robustness. In this paper, we introduce an effective object-level SLAM method that employs a stereo camera to enhance the navigation robustness of autonomous underwater vehicles and generates a detailed semantic map. Point features and object features are integrated to serve the proposed approach. We begin by detecting 2D objects in images using a state-of-the-art neural network, followed by obtaining 3D objects described by the general model through the principle of multi-view geometry and eventually constructing semantic landmarks. To account for object data association, we present an object match method that takes into consideration the stereo camera characteristics in a single stereo frame and a filter-based approach to track the landmarks in odometry. Experiments are also conducted using the KITTI dataset and our sequences collected from the pool and coast. The evaluation results indicate that the proposed method can improve the performance of ORBSLAM2 in terms of both navigation robustness and mapping information in underwater scenarios.

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Efficient Bathymetric SLAM With Invalid Loop Closure Identification

Cited by 15 publications

References 21 publications

Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar

Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar

Automatic Vocabulary and Graph Verification for Accurate Loop Closure Detection

An effective stereo SLAM with high-level primitives in underwater environment

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