Maritime navigation in low visibility presents a significant challenge, jeopardising seafarers’ situational awareness and escalating collision risks. This study introduces a maritime head-up display (mHUD) to address this issue. The mHUD, a 2-m diameter aluminium ring with dual rows of LEDs, enhances visibility for autonomous ships in adverse conditions on ship bridges and remote operating centres (ROCs). Displaying various modes such as shallow waters, land, lighthouses, beacons, buoys and maritime traffic, the mHUD was evaluated in a ship bridge simulator by 12 navigation students. Results revealed that the mHUD substantially improved situational awareness, proving more efficient and effective than navigating without it in poor visibility conditions. Participants found the mHUD easy to learn and expressed willingness to use it in real-world situations. The study highlights the mHUD’s potential to enhance situational awareness on ship bridges and ROCs for autonomous ships, while suggesting potential enhancements to increase usability and user satisfaction.

In this paper we address the problem of stabilization and local positioning of a four-rotor rotorcraft using computer vision. Our approaches to estimate the orientation and position of the rotorcraft combine the measurements from an Inertial Measurement Unit (IMU) and a vision system composed of a single camera. In the first stage, the vision system is used to estimate the position and yaw angle of the rotorcraft, while in the second stage the vision system is used to estimate the translational velocity of the flying robot. In both cases the IMU gives the pitch and roll angles at a higher rate. The technique used to estimate the position of the rotorcraft in the first stage combines the homogeneous transformation approach for the camera calibration process with the plane-based pose method for estimating the position. In the second stage, a navigation system using the optical flow is also developed to estimate the translational velocity of the aircraft. We present real-time experiments of stabilization and location of a four-rotor rotorcraft.

Map matching has been widely applied in car navigation systems as an efficient method to display the location of vehicles on maps. Various map-matching algorithms have been proposed. Inevitably, the correctness of the map matching is closely related to the accuracy of positioning sensors, such as GPS or Dead Reckoning (DR), and the complexity of the road network and map, especially in urban areas where the GPS signal may be constantly blocked by buildings and the road network is complicated. The existing map matching algorithms cannot resolve the positioning problems under all circumstances. They sometimes give the wrong position estimates of the car on road; the result is called mismatching. In order to improve the quality of map matching, a deep understand of the accuracy of sensor errors on mismatching is important. This paper analyses various factors that may affect the quality of map matching based on extensive tests in Hong Kong. Suggestions to improve the success rate of map matching are also provided.