With the continuous expansion of the application field of UAV intelligent systems to GNSS-denied environments, the existing navigation system can hardly meet low cost, high precision, and high robustness in such conditions. Most navigation systems used in GNSS-denied environments give up the connection between the map frame and the actual world frame, making them impossible to apply in practice. Therefore, this paper proposes a Lidar navigation system based on global ArUco, which is widely used in large-scale known GNSS-denied scenarios for UAVs. The system jointly optimizes the Lidar, inertial measurement unit, and global ArUco information by factor graph and outputs the pose in the real-world frame. The system includes a method to update the global ArUco confidence with sampling, improving accuracy while using the pose solved from the global ArUco. The system uses the global ArUco to maintain navigation when Lidar is degraded. The system also has a loop closure determination part based on ArUco, which reduces the consumption of computing resources. The navigation system has been tested in the dry coal shed of a thermal power plant using a UAV platform. Experiments demonstrate that the system can achieve global, accurate, and robust pose estimation in large-scale, complex GNSS-denied environments.
With its fast and accurate position and attitude estimation, the feature-based lidar-inertial odometer is widely used for UAV navigation in GNSS-denied environments. However, the existing algorithms cannot accurately extract the required feature points in the spatial grid structure, resulting in reduced positioning accuracy. To solve this problem, we propose a lidar-inertial navigation system based on grid and shell features in the environment. In this paper, an algorithm for extracting features of the grid and shell is proposed. The extracted features are used to complete the pose (position and orientation) calculation based on the assumption of local collinearity and coplanarity. Compared with the existing lidar navigation system in practical application scenarios, the proposed navigation system can achieve fast and accurate pose estimation of UAV in a GNSS-denied environment full of spatial grid structures.
Strategic resources affect national development and security at all times. Actual utilization of strategic resources is crucial. In recent years, strategic resources such as coal and mineral powder usually need to be stored in closed stockyards after mining due to environmental protection, concealment, and other reasons. The existing stockpiling inventory system cannot autonomously complete the stockpiling inventory in the closed stockyard with low risk, low cost, and high precision. Therefore, this paper proposes a closed stockyard UAV intelligent inventory system. Our inventory system comprises a UAV system, a visual mapping system, and a classification and volume calculation system. In this paper, we focus on the navigation system belonging to the UAV system and propose a GNSS-denied environment navigation system based on lidar and ArUco markers. We conduct experiments in the dry coal shed of thermal power plants, one of the application scenarios. After analysis and comparison, it can be concluded that our intelligent UAV stockpiling inventory system can complete the task with high robustness and high precision with the assistance of our navigation system.
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