# UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications

arXiv: https://arxiv.org/abs/2401.06407 · HTML: https://mlsanity.com/paper/2401.06407/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 51 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 11 of 11)

- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Pixel-Perfect Structure-from-Motion with Featuremetric Refinement](https://mlsanity.com/paper/2108.08291/) (2021)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras](https://mlsanity.com/paper/1708.07878/) (2017)
- [PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2203.12082/) (2022)
- [FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation](https://mlsanity.com/paper/1905.11377/) (2019)
- [Fast, Autonomous Flight in GPS-Denied and Cluttered Environments](https://mlsanity.com/paper/1712.02052/) (2017)
- [VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach](https://mlsanity.com/paper/2010.12274/) (2020)
- [High-Performance and Tunable Stereo Reconstruction](https://mlsanity.com/paper/1511.00758/) (2015)
- [DSOL: A Fast Direct Sparse Odometry Scheme](https://mlsanity.com/paper/2203.08182/) (2022)

## Abstract (arXiv)

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.