Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in an unknown cluttered environment. However, real-time and stability remain a significant challenge in the field of path planning. To improve the robustness and efficiency of the path planning method in complex environments, this paper presents RETRBG, a robust and efficient trajectory replanning method based on the guiding path. Firstly, a safe guiding path is generated by using an improved A* and path pruning method, which is used to perceive the narrow space in its surrounding environment. Secondly, under the guidance of the path, a guided kinodynamic path searching method (GKPS) is devised to generate a safe, kinodynamically feasible and minimum-time initial path. Finally, an adaptive optimization function with two modes is proposed to improve the optimization quality in complex environments, which selects the optimization mode to optimize the smoothness and safety of the path according to the perception results of the guiding path. The experimental results demonstrate that the proposed method achieved good performance both in different obstacle densities and different resolutions. Compared with the other state-of-the-art methods, the quality and success rate of the planning result are significantly improved.
LiDAR is a crucial sensor for 3D environment perception. However, limited by the field of view of the LiDAR, it is sometimes difficult to achieve complete coverage of the environment with a single LiDAR. In this paper, we designed a spinning actuated LiDAR mapping system that is compatible with both UAV and backpack platforms and propose a tightly coupled laser–inertial SLAM algorithm for it. In our algorithm, edge and plane features in the point cloud are first extracted. Then, for the significant changes in the distribution of point cloud features between two adjacent scans caused by the continuous rotation of the LiDAR, we employed an adaptive scan accumulation method to improve the stability and accuracy of point cloud registration. After feature matching, the LiDAR feature factors and IMU pre-integration factor are added to the factor graph and jointly optimized to output the trajectory. In addition, an improved loop closure detection algorithm based on the Cartographer algorithm is used to reduce the drift. We conducted exhaustive experiments to evaluate the performance of the proposed algorithm in complex indoor and outdoor scenarios. The results showed that our algorithm is more accurate than the state-of-the-art algorithms LIO-SAM and FAST-LIO2 for the spinning actuated LiDAR system, and it can achieve real-time performance.
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