This article presents a comprehensive review of and analysis of state-of-the-art mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on canopy-level and high-speed scenarios. This article presents a comprehensive exploration of sensor technologies suitable for UAV mapping, assessing their capabilities to provide measurements that meet the requirements of fast UAV mapping. Furthermore, the study conducts extensive experiments in a simulated environment to evaluate the performance of three distinct mapping algorithms: Direct Sparse Odometry (DSO), Stereo DSO (SDSO), and DSO Lite (DSOL). The experiments delve into mapping accuracy and mapping speed, providing valuable insights into the strengths and limitations of each algorithm. The results highlight the versatility and shortcomings of these algorithms in meeting the demands of modern UAV applications. The findings contribute to a nuanced understanding of UAV mapping dynamics, emphasizing their applicability in complex environments and high-speed scenarios. This research not only serves as a benchmark for mapping algorithm comparisons but also offers practical guidance for selecting sensors tailored to specific UAV mapping applications.

翻译：本文对面向无人机（UAV）应用的先进建图算法进行了全面综述与分析，重点关注冠层级与高速场景。文章系统探讨了适用于无人机建图的传感器技术，评估了这些传感器提供满足快速无人机建图需求的测量数据的能力。此外，本研究在仿真环境中开展了大量实验，以评估三种不同建图算法的性能：直接稀疏里程计（DSO）、立体DSO（SDSO）和DSO Lite（DSOL）。实验深入研究了建图精度与建图速度，揭示了每种算法的优势与局限。结果凸显了这些算法在满足现代无人机应用需求方面的适应性与不足。研究结论有助于深入理解无人机建图动态，强调了其在复杂环境与高速场景中的适用性。本研究不仅为建图算法比较提供了基准，也为针对特定无人机建图应用的传感器选择提供了实用指导。