Obstacle Tracking for Unmanned Surface Vessels Using 3-D Point Cloud

Muhovič, Jon; Mandeljc, Rok; Bovcon, Borja; Kristan, Matej; Perš, Janez

doi:10.1109/joe.2019.2909507

Cited by 63 publications

(28 citation statements)

References 23 publications

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“…are being introduced and mounted onboard. [29] uses three dimensional point cloud produced by LiDAR to detect objects. Similarly, [30] uses LiDAR to generate a stable navigable region for ASVs.…”

Section: B Obstacle Detection For Asv In Maritime Environmentsmentioning

confidence: 99%

WODIS: Water Obstacle Detection Network Based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments

Chen

Liu

Achuthan

2021

IEEE Trans. Instrum. Meas.

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A reliable obstacle detection system is crucial for Autonomous Surface Vehicles (ASVs) to realise fully autonomous navigation with no need of human intervention. However, the current detection methods have particular drawbacks such as poor detection for small objects, low estimation accuracy caused by water surface reflection and a high rate of false-positive on water-sky interference. Therefore, we propose a new encoderdecoder structured deep semantic segmentation network, which is Water Obstacle Detection network based on Image Segmentation (WODIS), to solve above mentioned problems. The first design feature of WODIS utilises the use of an encoder network to extract high-level data based on different sampling rates. In order to improve obstacle detection at sea-sky-line areas, an Attention Refine Module (ARM) activated by both global average pooling and max pooling to capture high-level information has been designed and integrated into WODIS. In addition, a Feature Fusion Module (FFM) is introduced to help concatenate the multi-dimensional high-level features in the decoder network. The WODIS is tested and cross validated using four different types of maritime datasets with the results demonstrating that mIoU of WODIS can achieve superior segmentation effects for sea level obstacles to values as high as 91.3%.

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Section: B Obstacle Detection For Asv In Maritime Environmentsmentioning

confidence: 99%

WODIS: Water Obstacle Detection Network Based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments

Chen

Liu

Achuthan

2021

IEEE Trans. Instrum. Meas.

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“…However, in the area of unmanned surface vehicles (USV), most efforts have been put in the development, adaptation or integration of sensors, electronics, and algorithms (Campbell et al 2012;Liu et al 2016), while the hull configurations of autonomous marine platforms remain relatively simple from the hydrodynamic standpoint. Some examples of recent studies include three-degree-of-freedom propulsion modeling and experiments on USV (Mu et al 2018), distributed containment maneuvering controllers for underactuated USV's (Gu et al 2019), and development of a novel obstacle tracking method for USV (Muhovic et al 2019). Small, about 1-meterlong USVs comparable in size to the boats described in this paper have been also constructed by researchers, including airboats (Valada et al 2012), catamarans (Sohn et al 2015), and fast planing hulls (Grenestedt et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Development of small-scale unmanned hydrofoil boats

Thompson

Whitworth

Matveev

2021

J. Unmanned Veh. Sys.

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Unmanned boats have gained a lot of interest in the maritime community during the last decade. Most hydrodynamic platforms employed for unmanned boats are based on traditional relatively simple hulls. In the present study, small-scale hydrofoil-assisted unmanned boats (0.6-0.7 meter in length and 3.5-5.5 kg in mass) have been developed and tested. Design calculations using a hydrodynamic transverse-strip engineering method with semi-empirical correlations were applied to determine suitable dimensions for hydrofoil systems. The boat hulls and hydrofoils were fabricated by laying up carbon-fiber cloth sheets on foam cores or 3D printed profiles. The boats were instrumented with outboard propulsors and electronic equipment for operations in both remote control and autopilot modes. In addition, an in-situ thrust-measuring module was designed and installed at the hull sterns to gather thrust data at GPS-measured speeds in the range between 0 to 11 m/s. The developed boats proved to be robust platforms capable of going over 600 m distances at high speeds while autonomously following preset paths. The presented methods and results can assist engineers developing unmanned surface vehicles that utilize advanced hydrodynamic concepts.

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“…To achieve superior perception performance, the USV generally requires employing heterogeneous sensors covering radar, lidar, camera, and infrared sensors [ 6 ]. They provide advantages of computer vision in terms of power consumption, size, weight, cost, and the readability of data, unlike radar or LIDAR, which may require heavy equipment placed on the vehicle [ 7 , 8 , 9 ]. Therefore, vision-based target detection on the sea for USVs has received much attention.…”

Section: Introductionmentioning

confidence: 99%

A Novel Target Detection Method of the Unmanned Surface Vehicle under All-Weather Conditions with an Improved YOLOV3

Guo

et al. 2020

Sensors

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The USV (unmanned surface vehicle) is playing an important role in many tasks such as marine environmental observation and maritime security, for the advantages of high autonomy and mobility. Detecting the targets on the surface of the water with high precision ensures the subsequent task implementation. However, the changes from the lights and the surface environment influence the performance of the target detecting method in a long-term task with USV. Therefore, this paper proposed a novel target detection method by fusing DenseNet in YOLOV3 to improve the stability of detection to decrease the feature loss, while the target feature is transmitted in the layers of a deep neural network. All the image data used to train and test the proposed method were obtained in the real ocean environment with a USV in the South China Sea during a one month sea trial in November 2019. The experiment results demonstrate the performance of the proposed method is more suitable for the changed weather conditions though comparing with the existing methods, and the real-time performance is available in practical ocean tasks for USV.

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Obstacle Tracking for Unmanned Surface Vessels Using 3-D Point Cloud

Cited by 63 publications

References 23 publications

WODIS: Water Obstacle Detection Network Based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments

WODIS: Water Obstacle Detection Network Based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments

Development of small-scale unmanned hydrofoil boats

A Novel Target Detection Method of the Unmanned Surface Vehicle under All-Weather Conditions with an Improved YOLOV3

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