A Method with Improved Accuracy and Robustness for Object Detection in Wharf Scenarios

Wu, Jingxuan; Song, Yuying; Song, Chunyi; Xu, Zhiwei

doi:10.1109/radarconf2248738.2022.9764241

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…This difficulty can be handled by using a combination of heterogeneous sensors, such as cameras, LiDARs, marine radars, and other navigational aids, as these sensors have different detection ranges and characteristics that can complement each other. Some studies (Cheng et al, 2021;Clunie et al, 2021;Elkins et al, 2010;Haghbayan et al, 2018;Han et al, 2020Han et al, , 2017Helgesen et al, 2019Helgesen et al, , 2022Hermann et al, 2015;Kim et al, 2021;Liu et al, 2022;Wu et al, 2022) have addressed multisensor fusion and tracking of maritime objects. While most of the existing studies have focused on sensor fusion for object detection in an open sea, we applied the detection algorithm to cover a large range from narrow waterways to open nearshore environments.…”

Section: Related Workmentioning

confidence: 99%

Field experiment of autonomous ship navigation in canal and surrounding nearshore environments

Kim,

Lee,

Chung

et al. 2023

Journal of Field Robotics

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In this paper, we present the development of autonomous navigation capabilities for small cruise boats, and their verification by field experiments in a canal and its surrounding waters. A cruise boat was converted to an autonomous surface vehicle (ASV) by installing various sensors and actuators to enable autonomous navigation. Navigation and perception sensors, such as global positioning system, attitude and heading reference system, radar, light detection and ranging (LiDAR), and cameras, were mounted on the ASV to estimate its motion and perceive the surrounding environment. Motors and potentiometers were installed for active control of the ASV. Software system components including navigation filters, object‐detection, path‐planning, and control algorithms were designed and implemented. In the narrow canal region, LiDARs were used to detect the side walls and boundaries of the canal. In open areas outside the canal, obstacles and object features were detected using various combinations of onboard sensors. A model‐based path‐planning algorithm was designed to avoid the detected obstacles, and the line‐of‐sight guidance was employed to control the vehicle. The performance of the developed system was verified through a field experiment in a real‐world maritime environment.

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