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1. Contour based Reconstruction of Underwater Structures Using Sonar, Visual, Inertial, and Depth Sensor

   Sharmin Rahman, Alberto Quattrini (October 2019, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This paper presents a systematic approach on realtime reconstruction of an underwater environment using Sonar, Visual, Inertial, and Depth data. In particular, low lighting conditions, or even complete absence of natural light inside caves, results in strong lighting variations, e.g., the cone of the artificial video light intersecting underwater structures, and the shadow contours. The proposed method utilizes the well defined edges between well lit areas and darkness to provide additional features, resulting into a denser 3D point cloud than the usual point clouds from a Visual SLAM system. Experimental results in an underwater cave at Ginnie Springs, FL, with a custommade underwater sensor suite demonstrate the performance of our system. This will enable more robust navigation of AUVs using the denser 3D point cloud to detect obstacles and achieve higher resolution reconstructions. 

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2. Wireless Sensing for Structural Health Monitoring of Bridges by Unmanned Aerial Vehicle (UAV) via BLE Communication

   Afonso, Bryce; Liu, Hong (May 2024, 2024 International Wireless Communications and Mobile Computing (IWCMC))

   The Internet of Things (IoT) has significantly advanced the application of Wireless Sensor Networks (WSNs) in Structural Health Monitoring (SHM), particularly for civil engineering infrastructure. While unmanned aerial vehicles (UAVs) are commonly employed for data collection, this paper proposes a novel approach using Bluetooth Low Energy (BLE) for synchronization and data gathering in SHM systems. Unlike traditional methods that may suffer from compromised network security and increased energy demands, the BLE-based system ensures that individual sensor nodes operate autonomously, providing inherent security benefits and improved battery longevity. Each sensor node acts independently, minimizing the risk to the overall network if a single node is compromised. We present a synchronization scheme that leverages BLE's low-power consumption to enhance the SHM of bridges, supported by a prototype developed using a PASCO bridge kit with wireless load cells and accelerometers. The proposed BLE protocol, to the best of the authors' knowledge, represents an unexplored avenue in SHM, promising increased safety and efficiency in sensor networks. 

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3. Wireless Ultrasonic Phased Arrays for On-Demand Wall Thickness Measurement and Damage Detection

   https://doi.org/10.1115/SMASIS2025-167971  

   Bo, Luyu; Li, Jiali; Li, Teng; Cai, Bowen; Wang, Zhide; Liu, Hongye; Moomaw, Andrew; Tian, Zhenhua (September 2025, American Society of Mechanical Engineers)

   Abstract Ultrasonics structural health monitoring (SHM) is widely recognized as an effective technique that enables early damage detection in large-scale structures and helps prevent potential catastrophic failures. Ultrasonic phased array technology has gained prominence in SHM due to its ability to inspect a large area with high spatial resolution. However, conventional systems often rely on physical wired sensor networks, limiting their deployment for hard-to-access regions. In this study, we present a wireless ultrasonic phased array system capable of dual-mode operation for both wall thickness measurement and structural damage detection. The system integrates wireless power transfer (WPT) modules and customized matching circuits, enabling efficient and flexible deployment. Proof-of-concept experiments demonstrate successful wall thickness evaluation and accurate defect localization in metallic structures using both delay-and-sum (DAS) and minimum variance (MV) imaging methods, with the MV algorithm offering improved imaging resolution. Future work will focus on advancing real-time monitoring through machine learning, enabling 3D imaging, and extending system applicability to anisotropic composite materials. 

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4. Multicamera 3D Reconstruction of Dynamic Surgical Cavities: Camera Grouping and Pair Sequencing

   https://doi.org/10.1109/ISMR.2019.8710190  

   Su, Yun-Hsuan; Huang, Kevin; Hannaford, Blake (April 2019, 2019 International Symposium on Medical Robotics (ISMR), Atlanta, GA, USA, 2019)

   null (Ed.)

   Dynamic 3D reconstruction of surgical cavities is essential in a wide range of computer-assisted surgical intervention applications, including but not limited to surgical guidance, pre-operative image registration and vision-based force estimation. According to a survey on vision based 3D reconstruction for abdominal minimally invasive surgery (MIS) [1], real-time 3D reconstruction and tissue deformation recovery remain open challenges to researchers. The main challenges include specular reflections from the wet tissue surface and the highly dynamic nature of abdominal surgical scenes. This work aims to overcome these obstacles by using multiple viewpoint and independently moving RGB cameras to generate an accurate measurement of tissue deformation at the volume of interest (VOI), and proposes a novel efficient camera pairing algorithm. Experimental results validate the proposed camera grouping and pair sequencing, and were evaluated with the Raven-II [2] surgical robot system for tool navigation, the Medtronic Stealth Station s7 surgical navigation system for real- time camera pose monitoring, and the Space Spider white light scanner to derive the ground truth 3D model. 

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5. A Deep Learning Approach to Localization for Navigation on a Miniature Autonomous Blimp

   https://doi.org/10.1109/ICCA51439.2020.9264514  

   Seguin, Landan; Zheng, Justin; Li, Alberto; Tao, Qiuyang; Zhang, Fumin (October 2020, IEEE International Conference on Control and Automation (ICCA))

   null (Ed.)

   The Georgia Tech Miniature Autonomous Blimp (GT-MAB) needs localization algorithms to navigate to way-points in an indoor environment without leveraging an external motion capture system. Indoor aerial robots often require a motion capture system for localization or employ simultaneous localization and mapping (SLAM) algorithms for navigation. The proposed strategy for GT-MAB localization can be accomplished using lightweight sensors on a weight-constrained platform like the GT-MAB. We train an end-to-end convolutional neural network (CNN) that predicts the horizontal position and heading of the GT-MAB using video collected by an onboard monocular RGB camera. On the other hand, the height of the GT-MAB is estimated from measurements through a time-of-flight (ToF) single-beam laser sensor. The monocular camera and the single-beam laser sensor are sufficient for the localization algorithm to localize the GT-MAB in real time, achieving the averaged 3D positioning errors to be less than 20 cm, and the averaged heading errors to be less than 3 degrees. With the accuracy of our proposed localization method, we are able to use simple proportional-integral-derivative controllers to control the GT-MAB for waypoint navigation. Experimental results on the waypoint following are provided, which demonstrates the use of a CNN as the primary localization method for estimating the pose of an indoor robot that successfully enables navigation to specified waypoints. 

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