Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators

Robust velocity and position estimation is crucial for autonomous robot navigation. The optical flow based methods for autonomous navigation have been receiving increasing attentions in tandem with the development of micro unmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC) based algorithm to determine optical flow using a monocular camera, which is named as correlation flow (CF). Correlation flow is able to provide reliable and accurate velocity estimation and is robust to motion blur. In addition, it can also estimate the altitude velocity and yaw rate, which are not available by traditional methods. Autonomous flight tests on a quadcopter show that correlation flow can provide robust trajectory estimation with very low processing power. The source codes are released based on the ROS framework.Comment: 2018 International Conference on Robotics and Automation (ICRA 2018

Pavement condition survey in road along Kampung Selancar Pagoh

Road maintenance involves in remedying defects such as potholes, patch, edge defects and etc. that occurs in the carriageway. The corrective maintenance and providing treatments is carried out from time to time to slow the rate of deterioration (preventative maintenance). Road maintenance is essential to preserve the road in its originally constructed condition, protect adjacent resources and user safety and provide, and convenient travel along the route. Before doing any road maintenance, the defects should be determined first. The place chosen in Kampung Selancar, Pagoh which is the defects determination are carried out along 1km carriageway..

Outdoor Autonomous Landing of a Quadcopter on a Moving Platform using Off-board Computer Vision

 This paper presents a method that enables a quadcopter to perform autonomous landing on a moving platform using computer vision. In addition, the system implementation of the computer vision technique is presented. Unlike other researches, the camera is mounted on the moving platform instead of being installed on the quadcopter. Besides, the computer vision system is tested outdoor, and the results such as the performance and the accuracy are presented. In the stationary platform test, 5 out of 10 landings fall within 30 cm from the center. In the moving platform test, the maximum platform-moving speed for autonomous landing is 2 m/s. Hence, it is proven that this methodology is feasible. Lastly, the advantages and limitations of the computer vision technique proposed are discussed

Autonomous wireless self-charging for multi-rotor unmanned aerial vehicles

Rotary-wing unmanned aerial vehicles (UAVs) have the ability to operate in confined spaces and to hover over point of interest, but they have limited flight time and endurance. Conventional contact-based charging system for UAVs has been used, but it requires high landing accuracy for proper docking. Instead of the conventional system, autonomous wireless battery charging system for UAVs in outdoor conditions is proposed in this paper. UAVs can be wirelessly charged using the proposed charging system, regardless of yaw angle between UAVs and wireless charging pad, which can further reduce their control complexity for autonomous landing. The increased overall mission time eventually relaxes the limitations on payload and flight time. In this paper, a cost effective automatic recharging solution for UAVs in outdoor environments is proposed using wireless power transfer (WPT). This research proposes a global positioning system (GPS) and vision-based closed-loop target detection and a tracking system for precise landing of quadcopters in outdoor environments. The system uses the onboard camera to detect the shape, color and position of the defined target in image frame. Based on the offset of the target from the center of the image frame, control commands are generated to track and maintain the center position. Commercially available AR.Drone. was used to demonstrate the proposed concept which is equppied with bottom camera and GPS. Experiments and analyses showed good performance, and about 75% average WPT efficiency was achieved in this research