This article presents an analysis of current state-of-the-art sensors and how these sensors work with several mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on low-altitude and high-speed scenarios. A new experimental construct is created using highly realistic environments made possible by integrating the AirSim simulator with Google 3D maps models using the Cesium Tiles plugin. Experiments are conducted in this high-realism simulated environment to evaluate the performance of three distinct mapping algorithms: (1) Direct Sparse Odometry (DSO), (2) Stereo DSO (SDSO), and (3) DSO Lite (DSOL). Experimental results evaluate algorithms based on their measured geometric accuracy and computational speed. The results provide valuable insights into the strengths and limitations of each algorithm. Findings quantify compromises in UAV algorithm selection, allowing researchers to find the mapping solution best suited to their application, which often requires a compromise between computational performance and the density and accuracy of geometric map estimates. Results indicate that for UAVs with restrictive computing resources, DSOL is the best option. For systems with payload capacity and modest compute resources, SDSO is the best option. If only one camera is available, DSO is the option to choose for applications that require dense mapping results.

翻译：本文分析了当前最先进的传感器及其与多种地图构建算法在无人机（UAV）应用中的协同工作方式，重点关注低空高速场景。通过集成AirSim模拟器与基于Cesium Tiles插件的Google 3D地图模型，构建了高逼真度环境下的新型实验平台。在该高仿真模拟环境中开展实验，评估三种不同地图构建算法的性能：（1）直接稀疏里程计（DSO）、（2）立体DSO（SDSO）及（3）DSO精简版（DSOL）。实验基于几何精度与计算速度两个维度对算法进行评价，结果揭示了各算法的优势与局限性。研究量化了无人机算法选择中的权衡关系，使研究人员能够根据实际应用需求寻找最适配的地图构建方案——这通常需要在计算性能与几何地图估计的密度及精度之间做出取舍。实验结果表明：对于计算资源受限的无人机，DSOL是最优选择；具备载荷能力且计算资源适中的系统宜选用SDSO；若仅能使用单摄像头且需获得密集地图结果，则DSO为优选算法。