Underwater Visual Tracking Method Based on KCF Algorithm of ArUco Marker

Ren, Ranzhen; Zhang, Lichuan; Yuan, Yijie; Wang, Zhimo; Liu, Lu

doi:10.1109/ieeeconf38699.2020.9389441

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…In the second phase, the study employed OpenCV's ArUco module to detect specific markers, obtaining the 6-degree-of-freedom position and orientation information relative to the camera. Building upon previous research, [26] introduced an underwater visual tracking method based on ArUco markers. With the Kernel Correlation Filter algorithm's acceleration strategy, this approach effectively addressed the AUV's short-range position sensing problem.…”

Section: Underwater Visual Docking and Guidance Systems Based On Pass...mentioning

confidence: 99%

Review of underwater visual navigation and docking: advances and challenges

Li,

Sun

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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The widespread utilization of autonomous underwater vehicles in marine science and engineering has underscored the paramount importance of precise underwater navigation and docking capabilities for underwater robots. Underwater vision navigation systems serve as a pivotal technology, encompassing various domains such as computer vision, image processing, and machine learning. Nevertheless, they confront a series of challenges. This review aims to consolidate the most recent research advancements in underwater vision navigation systems, with a particular focus on critical aspects such as beacon design, monocular and binocular vision, and detection algorithms. At present, underwater beacons can be categorized into active and passive types, with the flexibility to choose between them contingent upon the specific task and environment. On the other hand, relative to the prevalent use of monocular vision, binocular vision systems offer the potential to furnish more accurate depth information, and the seamless integration of monocular and binocular vision systems enhances the precision and stability of navigation tasks. Concerning detection algorithms, traditional feature extraction methods and deep learning approaches each exhibit their merits and drawbacks, but deep learning methods demonstrate immense potential in complex underwater environments. By summarizing and analyzing the above research results, the application of each key technology of underwater vision navigation and docking is sorted out, and its further development direction is foreseen.

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Section: Underwater Visual Docking and Guidance Systems Based On Pass...mentioning

confidence: 99%

Review of underwater visual navigation and docking: advances and challenges

Li,

Sun

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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“…Hence, he developed a method to improve the marker detection and extracted more information from the markers. Ren used a Kernel Correlation Filter (KCF) acceleration strategy for short sensing time [22]. A pool experiment was conducted to show the advantages of the method.…”

Section: Related Workmentioning

confidence: 99%

An Underwater Visual Navigation Method Based on Multiple ArUco Markers

Haroutunian

Murphy

et al. 2021

JMSE

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Underwater navigation presents crucial issues because of the rapid attenuation of electronic magnetic waves. The conventional underwater navigation methods are achieved by acoustic equipment, such as the ultra-short-baseline localisation systems and Doppler velocity logs, etc. However, they suffer from low fresh rate, low bandwidth, environmental disturbance and high cost. In the paper, a novel underwater visual navigation is investigated based on the multiple ArUco markers. Unlike other underwater navigation approaches based on the artificial markers, the noise model of the pose estimation of a single marker and an optimal algorithm of the multiple markers are developed to increase the precision of the method. The experimental tests are conducted in the towing tank. The results show that the proposed method is able to localise the underwater vehicle accurately.

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“…However, the paper does not provide a detailed analysis of the specific working conditions and performance of the ArUco code underwater. Ren et al (2020) also used the ArUco Marker as a short-range passive beacon and proposed the strategy of using the kernelized correlation filters tracker to increase the algorithm's speed.…”

Section: Introductionmentioning

confidence: 99%

“…The terminal docking of AUV has sprouted since the end of the last century and has formed a relatively mature system. According to the different types of sensors mounted on the AUV and the docking base station, the positioning methods of an AUV for terminal docking can be divided into acoustic positioning (Vallicrosa et al , 2014; Yue and Wang, 2016) electromagnetic positioning (Allen et al , 2006; VANDAVASI et al , 2018; Lin et al , 2022b) and visual positioning (Kim et al , 2014; Li et al , 2015; Palomeras et al , 2018; Lwin et al , 2018; Myint et al , 2018; Park et al , 2009; Li et al , 2016; Ren et al , 2020). Among them, the acoustic positioning data are updated slowly and are easily affected by sound reflection sources such as the target structure or the seabed; thus, the accuracy and stability of short-range positioning are poor (Fan et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

“…To address this issue, we propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons. Unlike previous literature (Bruno et al , 2019; Palomeras et al , 2018; Ren et al , 2020), which solely selected a single planar code as the positioning indicator, neglecting comparisons between various planar codes under distinct water parameters, this paper carries out a simulation experiment to scrutinize the comparative positioning performance of different planar codes under different water parameters. Additionally, simple visual pose estimation algorithms can result in output instability and poor real-time performance in complex water environments.…”

Section: Introductionmentioning

confidence: 99%

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Triangle codes and tracer lights based absolute positioning method for terminal visual docking of autonomous underwater vehicles

Zhang,

Zhong,

Lin

et al. 2024

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Purpose Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking. Design/methodology/approach This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm. Findings The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm. Originality/value This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

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Underwater Visual Tracking Method Based on KCF Algorithm of ArUco Marker

Cited by 5 publications

References 20 publications

Review of underwater visual navigation and docking: advances and challenges

Review of underwater visual navigation and docking: advances and challenges

An Underwater Visual Navigation Method Based on Multiple ArUco Markers

Triangle codes and tracer lights based absolute positioning method for terminal visual docking of autonomous underwater vehicles

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