Obstacle Detection in Real and Synthetic Harbour Scenarios

Faggioni, N.; Leonardi, Nicola; Ponzini, F.; Sebastiani, L.; Martelli, Michele

doi:10.1007/978-3-030-98260-7_2

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…Thus, the target was acquired and tracked with the perceptive system, comparing LiDAR and Camera results with the available ground truth value. Results are reported in Figures 13,14,15. In particular, Figure 13 shows the trajectory of the centroids evaluated with the camera (red markers) and with the LiDAR (blue markers). Figure 14 shows the error against the ground truth value on the x coordinates; finally, the same results are presented in a box plot in Figure 15.…”

Section: Resultsmentioning

confidence: 99%

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A multi-sensor indoor tracking system for autonomous marine model-scale vehicles

Ponzini,

Zaccone,

Martelli

2023

J. Phys.: Conf. Ser.

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Attitude estimation is a popular topic in marine engineering and robotics; the position and orientation of a vehicle are required as feedback from several control algorithms to improve autonomous navigation capabilities, such as dynamic positioning, track keeping, and autodocking. Typically, position and heading angles are provided by the Global Positioning System and compass. Usually, during the development and testing, the experiments are performed in a controlled environment, such as an indoor test tank. However, Global Positioning System systems can be unreliable due to non-negligible model scale errors or the absence of line-of-sight with the satellites. This article presents an experimental tracking system setup suitable for indoor testing facilities. In particular, the paper presents a tracking system based on a GigE camera and ArUco markers detection and a LiDAR-based tracking system relying on unsupervised machine learning techniques. The MQTT broker-based publish/subscribe message-queuing protocol allows real-time data communication and sharing. The proposed system was developed, installed, and tested in the COMPASS laboratory (University of Genoa). The two tracking systems’ outcomes have been compared. Eventually, an accuracy analysis was performed by comparing the results to the ground truth in purpose-built experiments. The proposed approach can estimate the degrees of freedom of a self-propelled model-scale vessel in an indoor testing facility without requiring active or powered markers and share the information acquired with multiple entities in real-time at a high frame rate.

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Section: Resultsmentioning

confidence: 99%

“…For these reasons, the cloud points exceeding the spatial limits of the test pool are filtered out. Moreover, considering the point position and intensity, additional noise filters were imposed to compensate for water acquisition errors, as presented in [13,14].…”

Section: Noise Filtering and Point-cloud Cuttingmentioning

confidence: 99%

A multi-sensor indoor tracking system for autonomous marine model-scale vehicles

Ponzini,

Zaccone,

Martelli

2023

J. Phys.: Conf. Ser.

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“…The potential of artificial intelligence in maritime navigation is well founded, both for object detection (Prasad et al, 2017) (Shao et al, 2018) (Kim et al, 2018) (Faggioni et al, 2022) and for trajectory prediction Tang et al (2022). The application of a Convolutional Neural Network (CNN) for object detection is further explored in this implementation.…”

Section: Perceptionmentioning

confidence: 99%

A Stereovision-based Navigation System for Autonomous Inland Vessels

Hepworth¹,

Garofano²,

Pang³

et al. 2022

Preprint

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The autonomous inland vessel has the potential to enable a hinterland modality shift to the waterway, however economically-conscious solutions to the technological challenges are lacking. This paper explores the potential role of stereovision technology as a primary sensor set within the application of autonomous inland vessels. The design of a multi-camera navigation system for mid-range perception and localisation tasks is presented with a view to providing sufficient data for collision avoidance tasks. The performance of these sensors in the application is evaluated with experimental testing using small-scale vessels in an indoor tank, demonstrating their capabilities and limitations for consideration in ongoing research.

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“…Infact, the use of a heterogeneous number of sensors with different peculiarities and measurement ranges allows the effectiveness of the system to be extended to broader scenarios, covering and balancing the shortcomings of individual sensors. A method for dealing with obstacle detection in LiDAR and cameras is proposed in (Faggioni et al, 2022).Figure 1 shows a possible application of the discussed procedure in a multi-sensor data fusion perceptive system. In paritcular, obstacles are detected in the LiDAR point cloud and the acquired targets are tracked by adopting a low computational customized method, instead of the probabilistic approaches generally employed (Ruud et al, 2018) (Lee et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Detecting and Tracking Multi-Object in Real Marine Environment

Martelli¹,

Faggioni²,

Ponzini³

2022

Preprint

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In recent years, the maritime sector has been witnessing a growing interest in autonomous navigation due to its advantages in safety. Within this framework, it is challenging to have available an automatic collision avoidance system based on data coming from the onboard sensors. For instance, LiDAR (Light Detection and Ranging) sensors are often employed to obtain a virtualized model of the surrounding environment in order to obtain an increase of situational awareness. Deep Learning approaches are widely used in the automotive sector for obstacle detection, particularly, trained Convolutional Neural Networks. Still, they imply a training phase based on a large dataset of pre-labelled LiDAR scans. Unfortunately, an extensive training dataset is not yet available for the marine environment. For such a reason, this paper presents in detail a low-computational alternative procedure based on Unsupervised Learning which overcomes the lack of a training dataset; each step is investigated, from the data sampling up to the multi-target tracking of the detected obstacles. In particular, the proposed object detection framework has been tested by means of an extensive on-field data collection campaign carried out during sea trials by equipping a ship with a LiDAR. Furthermore, Euclidean distance-based clustering algorithm and a bounding-box construction method based on Principal Component Analysis have been adopted. Moreover, a specific tracking system with no prediction filters is proposed to fulfil the strict time constraint for fast reactions in complex scenarios where several objects need to be tracked. The algorithm has been tested versus the well-known Global Nearest Neighbour tracker; such a comparison includes the computational cost and the results' accuracy. The whole approach has been tested on such a challenging and dynamic marine scenario, and the results obtained are presented and discussed. Such and outcome shows that the proposed approach can detect and track multiple objects with reasonable accuracy; moreover, the outputs are provided near real-time. To conclude, the pros, the weaknesses, and future developments are reported.

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Obstacle Detection in Real and Synthetic Harbour Scenarios

Cited by 3 publications

References 19 publications

A multi-sensor indoor tracking system for autonomous marine model-scale vehicles

A multi-sensor indoor tracking system for autonomous marine model-scale vehicles

A Stereovision-based Navigation System for Autonomous Inland Vessels

Detecting and Tracking Multi-Object in Real Marine Environment

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