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This article presents a comprehensive review of and analysis ofstate-of-the-art mapping algorithms for UAV (Unmanned Aerial Vehicle)applications, focusing on canopy-level and high-speed scenarios. This articlepresents a comprehensive exploration of sensor technologies suitable for UAVmapping, assessing their capabilities to provide measurements that meet therequirements of fast UAV mapping. Furthermore, the study conducts extensiveexperiments in a simulated environment to evaluate the performance of threedistinct mapping algorithms: Direct Sparse Odometry (DSO), Stereo DSO (SDSO),and DSO Lite (DSOL). The experiments delve into mapping accuracy and mappingspeed, providing valuable insights into the strengths and limitations of eachalgorithm. The results highlight the versatility and shortcomings of thesealgorithms in meeting the demands of modern UAV applications. The findingscontribute to a nuanced understanding of UAV mapping dynamics, emphasizingtheir applicability in complex environments and high-speed scenarios. Thisresearch not only serves as a benchmark for mapping algorithm comparisons butalso offers practical guidance for selecting sensors tailored to specific UAVmapping applications.

UAV-borne Mapping Algorithms for Canopy-Level and High-Speed Drone Applications