A Review of Visual SLAM Based on Unmanned Systems

Sun, Cheng-Zheng; Bo, Zhang; Wang, Jikai; Zhang, Chengshu

doi:10.1109/icaie53562.2021.00055

Cited by 12 publications

(6 citation statements)

References 40 publications

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“…These errors root in the estimations in the ( E , D ) space, where the standard deviation is 13.1 m and 7.6 m for tower A and tower B, respectively. In a similar low flight height, the localization precision is comparable to that in Masselli et al 52 and is higher than that in Sun et al 13 when the stereo camera is used. According to the jtop report, the system monitoring utility for Jetson stats, the highest resource consumption running the localization framework onboard is 98% of the GPU, 50% of the CPU, and 3.7 GB of the memory.…”

Section: Resultsmentioning

confidence: 47%

“…Popular RVL methods include visual odometry (VO) 12 and simultaneous localization and mapping (SLAM). 13 The core issue with RVL is similar to localization solely with the inertial measurement unit (IMU), namely the accumulation of error makes the localization drift over time. Although SLAM outperforms VO by mitigating the drift problem with bundle adjustment and loop closure detection, localization with long distance still needs regular correction by absolute localization information.…”

Section: Introductionmentioning

confidence: 99%

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Visual localization with a monocular camera for unmanned aerial vehicle based on landmark detection and tracking using YOLOv5 and DeepSORT

Dong²,

Zhao

et al. 2023

International Journal of Advanced Robotic Systems

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Absolute visual localization is of significant importance for unmanned aerial vehicles when the satellite-based localization system is not available. With the rapid evolution in the field of deep learning, the real-time visual detection and tracking of landmarks by an unmanned aerial vehicle could be implemented onboard. This study demonstrates a landmark-based visual localization framework for unmanned aerial vehicles flying at low altitudes. YOLOv5 and DeepSORT are used for multi-object detection and tracking, respectively. The unmanned aerial vehicle localization is achieved according to the geometric similarity between the geotagged transmission towers and the annotated images captured by a monocular camera. The validation is accomplished both in the Rflysim-based simulation and the quadrotor-based real flight. The localization precision is about 10 m, and the location update frequency reaches 5 Hz with a commercially available entry-level edge artificial intelligence platform. The proposed visual localization strategy needs no satellite image as a reference map, which saves a significant amount of the GPU memory and makes possible the end-to-end implementation on small unmanned aerial vehicles.

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Section: Resultsmentioning

confidence: 47%

Section: Introductionmentioning

confidence: 99%

Visual localization with a monocular camera for unmanned aerial vehicle based on landmark detection and tracking using YOLOv5 and DeepSORT

Dong²,

Zhao

et al. 2023

International Journal of Advanced Robotic Systems

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“…The choice between filtering and smoothing techniques introduced a trade-off between efficiency and accuracy. Moreover, map representations impacted interpretability, storage requirements, and computational complexity [ 31 ]. The development of you only look at coefficients with dynamic convolutions (YOLACT-Dyna) aimed to eliminate potential moving objects in the scene and provide an approximate camera pose estimation.…”

Section: Key Technologies and Influencing Factors Of Vslammentioning

confidence: 99%

A Comparative Review on Enhancing Visual Simultaneous Localization and Mapping with Deep Semantic Segmentation

Liu,

He,

et al. 2024

Sensors

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Visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded performance in dynamic environments featuring mobile objects. Recent research in deep learning led to notable progress in semantic segmentation, which involves assigning semantic labels to image pixels. The integration of semantic segmentation into VSLAM can effectively differentiate between static and dynamic elements in intricate scenes. This paper provided a comprehensive comparative review on leveraging semantic segmentation to improve major components of VSLAM, including visual odometry, loop closure detection, and environmental mapping. Key principles and methods for both traditional VSLAM and deep semantic segmentation were introduced. This paper presented an overview and comparative analysis of the technical implementations of semantic integration across various modules of the VSLAM pipeline. Furthermore, it examined the features and potential use cases associated with the fusion of VSLAM and semantics. It was found that the existing VSLAM model continued to face challenges related to computational complexity. Promising future research directions were identified, including efficient model design, multimodal fusion, online adaptation, dynamic scene reconstruction, and end-to-end joint optimization. This review shed light on the emerging paradigm of semantic VSLAM and how deep learning-enabled semantic reasoning could unlock new capabilities for autonomous intelligent systems to operate reliably in the real world.

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“…Underwater visual localization technology such as underwater visual SLAM is one of the key technologies for underwater inspection tasks. Underwater visual SLAM is utilized for the trajectory localization of underwater vehicles and the construction of the surrounding environment by feature matching and tracking the captured video images in the underwater scene [8,9]. Underwater navigation is necessary for the operation of autonomous underwater vehicles [10].…”

Section: Introductionmentioning

confidence: 99%

An Underwater Localization Method Based on Visual SLAM for the Near-Bottom Environment

Liu,

Wang,

et al. 2024

JMSE

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The feature matching of the near-bottom visual SLAM is influenced by underwater raised sediments, resulting in tracking loss. In this paper, the novel visual SLAM system is proposed in the underwater raised sediments environment. The underwater images are firstly classified based on the color recognition method by adding the weights of pixel location to reduce the interference of similar colors on the seabed. The improved adaptive median filter method is proposed to filter the classified images by using the mean value of the filter window border as the discriminant condition to retain the original features of the image. The filtered images are finally processed by the tracking module to obtain the trajectory of underwater vehicles and the seafloor maps. The datasets of seamount areas captured in the western Pacific Ocean are processed by the improved visual SLAM system. The keyframes, mapping points, and feature point matching pairs extracted from the improved visual SLAM system are improved by 5.2%, 11.2%, and 4.5% compared with that of the ORB-SLAM3 system, respectively. The improved visual SLAM system has the advantage of robustness to dynamic disturbances, which is of practical application in underwater vehicles operated in near-bottom areas such as seamounts and nodules.

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A Review of Visual SLAM Based on Unmanned Systems

Cited by 12 publications

References 40 publications

Visual localization with a monocular camera for unmanned aerial vehicle based on landmark detection and tracking using YOLOv5 and DeepSORT

Visual localization with a monocular camera for unmanned aerial vehicle based on landmark detection and tracking using YOLOv5 and DeepSORT

A Comparative Review on Enhancing Visual Simultaneous Localization and Mapping with Deep Semantic Segmentation

An Underwater Localization Method Based on Visual SLAM for the Near-Bottom Environment

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