This paper presents an independent stereo-vision based positioning system for docking operations. The low-cost system consists of an object detector and different 3D reconstruction techniques. To address the challenge of robust detections in an unstructured and complex outdoor environment, a learning-based object detection model is proposed. The system employs a complementary modular approach that uses data-driven methods, utilizing data wherever required and traditional computer vision methods when the scope and complexity of the environment are reduced. Both, monocular and stereo-vision based methods are investigated for comparison. Furthermore, easily identifiable markers are utilized to obtain reference points, thus simplifying the localization task. A small unmanned surface vehicle (USV) with a LiDAR-based positioning system was exploited to verify that the proposed vision-based positioning system produces accurate measurements under various docking scenarios. Field experiments have proven that the developed system performs well and can supplement the traditional navigation system for safety-critical docking operations.

An ever-increasing number of autonomous vehicles use bandwidth-greedy sensors such as cameras and LiDARs to sense and act to the world around us. Unfortunately, signal transmission in vehicles is vulnerable to passive and active cyber-physical attacks that may result in loss of intellectual property, or worse yet, the loss of control of a vehicle, potentially causing great harm. Therefore, it is important to investigate efficient cryptographic methods to secure signal transmission in such vehicles against outside threats. This study is motivated by the observation that previous publications have suggested legacy algorithms, which are either inefficient or insecure for vision-based signals. We show how stream ciphers and authenticated encryption can be applied to transfer sensor data securely and efficiently between computing devices suitable for distributed guidance, navigation, and control systems. We provide an efficient and flexible pipeline of cryptographic operations on image and point cloud data in the Robot Operating System (ROS). We also demonstrate how image data can be compressed to reduce the amount of data to be encrypted, transmitted, and decrypted. Experiments on embedded computers verify that modern software cryptographic algorithms perform very well on large sensor data. Hence, the introduction of such algorithms should enhance security without significantly compromising the overall performance.

Driven by advances in information and communication technologies, an increasing number of industries embrace unmanned and autonomous vehicles for services, such as public transportation, shipping, mapping, and remote surveillance. Unfortunately, these vehicles are vulnerable to passive and active cyber-physical attacks that can be used for industrial espionage and hijacking attempts. Since attackers can use hijacked vehicles as weapons in terrorist attacks, ensuring the secure operation of such vehicles is critical to prevent the attacks from causing dire financial consequences, or worse, the loss of human lives. This study is motivated by the observation that most cybersecurity studies provide superficial, high-level descriptions of vulnerabilities and attacks, and the true impact of the described attacks remains unclear. To address this problem, we demonstrate advanced manipulation attacks against an underactuated Unmanned Surface Vehicle (USV) which results in successful hijackings. Using state-of-the-art cryptography, we also show how the signal transmission can be secured to avoid hijacking attempts actively steering the vehicle off course. Through field experiments, we demonstrate how the attacks affect the closed-loop guidance, navigation, and control system and how the proposed countermeasures prevent these attacks from being successful. Our study is unique in that we provide a complete description of the attacked USV and give a detailed analysis of how spoofed navigation estimates affect the closed-loop behavior of the underactuated USV.

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