Jungwook Han

Precision navigation and mapping under bridges with an unmanned surface vehicle

Navigation relative to the surrounding physical structures and obstacles is an important capability for safe vehicle operation. This capability is particularly useful for unmanned surface vehicles (USVs) operating near large structures such as bridges, waterside buildings, towers and cranes, where global positioning system signals are restricted or unavailable due to the line-of-sight restrictions. This study proposes a high-precision navigation technique using dead-reckoning sensors and lidars, which enables building a parameterized map of artificial bridge structures and estimating the vehicle’s position relative to the parameterized map simultaneously. Also, three-dimensional reconstruction of the surrounding structures is carried out by fusing camera and lidar measurements for realistic 3D visual mapping which may facilitate automated surveys and inspection of structural safety. Field experiments using a newly developed USV system in a real-world bridge environment were performed to verify and demonstrate the performance of the proposed navigation and mapping algorithms. The field test results are presented and discussed in this paper.

Development of an Unmanned Surface Vehicle System for the 2014 Maritime RobotX Challenge

This paper addresses the development of an unmanned surface vehicle (USV) system by Team Angry-Nerds from KAIST for the inaugural Maritime RobotX Challenge competition, which was held on October 20-26, 2014, in Marina Bay, Singapore. The USV hardware was developed on a catamaran platform by integrating various system components, including propulsion, sensors, computer, power, and emergency systems. The competition comprised five mission tasks: 1) navigation and control, 2) underwater search and report, 3) automatic docking, 4) buoy search and observation, and 5) obstacle detection and avoidance. Onboard intelligence was a key factor for all of the mission tasks which needed to be performed autonomously with no human intervention. Software algorithms for vehicle autonomy were developed, and executable computer codes were implemented and integrated with the developed USV hardware system. This paper describes the development process of the USV system and its application to the competition mission tasks.

Navigation of an unmanned surface vessel under bridges

Obstacle detection and localization relative to surrounding objects or structures are important for safe vehicle navigation. This capability is particularly useful for unmanned surface vessels (USVs) operating near large structures that will block GPS signals. This study proposes a relative navigation approach, which allows estimating the vehicles position under bridges where GPS signals are not available by building a parameterized map of bridge pier structures in the framework of simultaneous localization and mapping (SLAM). The feasibility of the algorithm is demonstrated in outdoor experiments.

Three-dimensional reconstruction of a semi-submersible offshore platform with an unmanned surface vehicle

This paper addresses the three-dimensional (3D) reconstruction of a floating structure with an unmanned surface vehicle (USV). Onboard lidar and sonar sensors are employed to collect a volumetric point cloud of the structure both above and below the waterline. These measurements are obtained in the vehicle-fixed frame; thus, for successful 3D reconstruction, precision estimation of trajectory and attitude is required. GPS signals are severely deteriorated or unavailable near and under floating structures. Therefore, relative navigation with respect to the planar surfaces of their hull structures is performed in the framework of simultaneous localization and mapping (SLAM). This approach enables high-precision navigation and mapping near and under a large floating structure. A field experiment was performed in a semi-submersible offshore platform environment and the results are presented.

Persistent automatic tracking of multiple surface vessels by fusing radar and lidar

This paper addresses the problem of automatic target tracking of multiple surface vessels for unmanned surface vehicles (USVs). For safe USV operation, the detection of vessels in the surrounding environment is an important capability, and marine radars have been used to detect and estimate their motion. However, vessel detection at short-range using radars is challenging due to their inherent shadow zone. Therefore, we proposes a vessel tracking approach fusing a pulse radar and a 3D lidar. The relative bearing and range information between a USV and nearby vessels is obtained using radar and lidar sensors, and their motion including the position, heading, and speed is estimated based on a dual filter structure using an extended Kalman filter (EKF). This approach enables persistent tracking of multiple surface vessels. To verify and demonstrate the feasibility of the proposed method, a field experiment was performed in an inland river environment and the results are presented.

Planar SLAM under a semi-submersible offshore platform with an unmanned surface vehicle

This paper addresses relative navigation of unmanned surface vehicles (USVs) under marine floating structures where GPS signals are severely deteriorated or not available. An onboard 3D lidar collects point cloud of the structures and planar surfaces of their column structures are detected and used as landmark features in the framework of simultaneous localization and mapping (SLAM). The feasibility of the proposed method has been demonstrated in field experiments.

Three-dimensional reconstruction of bridge structures above the waterline with an unmanned surface vehicle

This study addresses three-dimensional (3D) reconstruction of bridge structures over water by fusing sensor measurements from inertial sensors, cameras and lidars mounted on an unmanned surface vehicle (USV). While the accurate navigation capability is strongly required for successful 3D reconstruction, global positioning system (GPS) signals which are essential for accurate navigation are severely deteriorated near the bridge structures or almost completely blocked underneath the bridge decks. In this study, a parameterized feature map is introduced by augmenting the map state with the geometric parameters of the detected bridge piers, and relative navigation is performed with respect to this map in the framework of simultaneous localization and mapping (SLAM). This parameterized SLAM approach allows for high-precision navigation and mapping with no need of GPS fixes. The feasibility of the proposed algorithm was demonstrated through field experiments.