Autonomous positioning of UAV based on puzzle descriptor in denial environment
In order to address the issues of long search time and high mismatch rates in UAV autonomous positioning based on point cloud matching under GNSS-denied environments,this paper proposes a two-view global point cloud registration algorithm based on Puzzle descriptors.The algorithm employs an improved ScanContext point cloud descriptor for segmenting and encoding the point cloud,leading to the design of a novel Puzzle descriptor.SIFT matching and template matching are applied to determine the pre-search area,followed by precise localization using ICP matching.The algorithm effectively reduces search time and mismatch rates,while also offering strong noise suppression capabilities.Simulation results show that the 3D positioning error is controlled within±20 cm,and the time for a single matching process is less than 1 second.Real-world flight test data further validate the algorithm's performance,with an average UAV positioning error of 19.94 cm and an average single matching time of 0.59 seconds,meeting the requirements for both accuracy and real-time UAV positioning.
万方数据
维普
