Recently, deep learning-based visual localization has gained significant attention and made remarkable advancements. Although previous visual localization methods have obtained promising performance on indoor or outdoor street scenes, there have been few attempts at visual localization on aerial scenes. In this article, a depth-aware aerial localization transformer (DALTR) is proposed to learn camera poses in real-world aerial scenes assisted by the depth map. To improve the ability of network to perceive on aerial scenes, a multi-level depth embedding transformer module is presented by adaptively incorporating depth information into multiple levels of transformer. In addition, to encourage the piece-wise smooth geometric characteristic of the scene coordinates, a depth-guided smoothness constraint is developed to provide additional supervision for scene coordinate regression. Extensive experimental results on aerial localization benchmark datasets demonstrate that the proposed DALTR achieves superior aerial localization performance.

This paper proposes a whole process method of multi-quadrotors from detecting and locating to encircle and track targets. The reconnaissance quadrotor realizes accurate target detection based on the one-stage target detector of convolutional neural network. Then, based on a pinhole camera projection model, the target is located from the 2D pixel coordinates to 3D North East Down(NED) coordinates world. Finally, the hunter quadrotors realize the target encircling and the time-varying formation tracking based on the consensus theory. At the same time, we prove the stability of the time-varying formation tracking control. We built a multiple quadrotors platform composed of one reconnaissance quadrotor and four hunter quadrotors, and deployed the method on the platform to conduct a series of experiments with a minibus as the target for validation. The results indicate that reconnaissance quadrotor can accurately detect target and have small localization errors in the north and east directions. Hunter quadrotors can encircle and track targets in time-varying formation based on target information provided by reconnaissance quadrotor. Experiments have demonstrated that the method achieves high-speed and accurate target encirclement.

This paper presents a collaborative control method based on payload-leading for the multi-quadrotor transportation systems. The goal is to keep the relative distance between the quadrotors and the payload as constant as possible during the transportation, so as to ensure the stable attitude of the payload. The control mechanism consists of a guidance control law that generates the common desired velocity for the quadrotors, an internal feedback controller for each quadrotor, and a decentralized formation controller. The stability of the control structure is proved by Lyapunov theory. Finally, the experimental platform of the multi-quadrotor transportation system is built to verify the effectiveness of the control method. Experimental results show that the proposed method has an excellent control effect.

In the 2nd International Unmanned Aerial Vehicle (UAV) Innovation Grand Prix (UAVGP) sponsored by Aviation Industry Corporation of China (AVIC), an autonomous barrel transportation mission was proposed for Rotary-wing UAV (RUAV) which aims at validating the feasibility of marine vertical replenishment by RUAVs. The RUAV was supposed to track the movable platform, pick up barrels on the supplier platform, stack barrels on the replenishment platform, take off and land autonomously without any human interference. The final score was determined based on the mission completion time and the stacking accuracy. The main challenges involved in this mission include attitude stabilizing with varying payload, accurate movable platform tracking and task-scheduling. An unmanned helicopter (UH) automatic barrels transportation system was established by our team, and the navigation system, control system and task scheduling system will be detailed in this paper, along with the flight tests before and in the competition. The system's performance has been verified in the competition and won the first prize finally.