  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.
