© 2021 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
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Environmental perception and monitoring play essential roles in maritime automation. Besides radar, the use of LiDAR for maritime surveillance has been increasing in recent years thanks to its high accuracy, high data density and good robustness against varying lighting conditions. This paper presents a novel approach for an adaptive shape-fitting technique using LiDAR point clouds in maritime applications, improving the object-tracking performance. The clustered LiDAR point clouds are fitted into bounding boxes or elliptic cylinders depending on their geometric shapes. A fitting score based on mean squared error is used for the shape decision. Afterwards, the extracted objects are associated with those in the past frames and tracked using an adaptive extended Kalman filter. The proposed algorithm is validated in simulation and post-processing using real-world test data. In simulations, the proposed adaptive shape-fitting technique shows a high object positioning and heading accuracy and guarantees a good object-tracking behaviour with a positioning error of 1.5 m. The proposed algorithm’s efficiency and robustness are further validated using test data recorded in the real-world using an unmanned surface vehicle equipped with LiDAR and GNSS in Rostock harbour, Germany. Test results show that the proposed adaptive shape-fitting technique helps the multi-object tracker reach a 2D position error of approximately 2 m with an update rate of 10 Hz, which is sufficient for object tracking in maritime applications. The size accuracy is improved by 10%, and heading accuracy is improved by 16% compared with multi-object tracking approaches only using L-shape fitting.
maritime surveillance, multi-object tracking, object detection, shape fitting
[1] Gehrt J.-J., Zweigel R., Roy S., Bueskens C., Kurowski M., Jeinsch T., Schubert A.,Gluch M., Simanski O., Pairet-Garcia E., Bruhn W., Diegel F. & D. Abel, Optimalmaneuvering and control of cooperative vessels within harbors. Journal of Physics:Conference Series, 1357, pp. 1-12, 2019.
[2] Bole A.G., Wall A.D. & Norris A., Radar and ARPA manual: radar, AIS and targettracking for marine radar users, 3 ed., Elsevier, 2014.
[3] Elkins L., Sellers D. & Monach W.R., The autonomous maritime navigation (AMN)project: field tests, autonomous and cooperative behaviors, data fusion, sensors, andvehicles. Journal of Field Robotics, 27, pp. 790-818, 2010.
[4] Han J., Cho Y., Kim J., Kim J., Son N.-S. & Kim S.Y., Autonomous collision detectionand avoidance for ARAGON USV: development and field tests. Journal of FieldRobotics, 37, pp. 987-1002, 2020.
[5] Ruud K., Brekke E. & Eidsvik J., LIDAR extended object tracking of a maritimevessel using an ellipsoidal contour model. 2018 Sensor Data Fusion: Trends, Solutions,Applications (SDF), pp. 1-6, 2018.
[6] Ye Y., Fu L. & Li B., Object detection and tracking using multi-layer laser forautonomous urban driving. 2016 IEEE 19th International Conference on IntelligentTransportation Systems (ITSC), pp. 259-264, 2016.
[7] Zhang X., Xu W., Dong C. & Dolan J.M., Efficient L-shape fitting for vehicle detectionusing laser scanners. 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 54-59, 2017.
[8] Shen X., Pendleton S. & Ang M.H., Efficient L-shape fitting of laser scanner datafor vehicle pose estimation. 2015 IEEE 7th International Conference on Cyberneticsand Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation andMechatronics (RAM), pp. 173-178, 2015.
[9] Halir R. & Flusser J., Numerically stable direct least squares fitting of ellipses, pp. 1-8,1998.
[10] Lin J., Koch L., Kurowski M., Gehrt J.-J., Abel D. & Zweigel R., Environmentperception and object tracking for autonomous vehicles in a harbor scenario. 23rd IEEEInternational Conference on Intelligent Transportation Systems (ITSC), pp. 1513-1518,2020.
[11] Bingham B., Aguero C., McCarrin M., Klamo J., Malia J., Allen K., Lum T., RawsonM. & Waqar R., Toward maritime robotic simulation in Gazebo. OCEANS 2019 MTS/IEEE SEATTLE, pp. 1-10, 2019.
[12] Kurowski M., Roy S., Gehrt J., Damerius R., Büskens C., Abel D. & Jeinsch T., Multivehicleguidance, navigation and control towards autonomous ship maneuvering inconfined waters. 2019 18th European Control Conference (ECC), pp. 2559-2564, 2019.