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

Jincheng Zhang, Artur Wolek, Andrew R. Willis

TL;DR

This work tackles the challenge of mapping with low-altitude, high-speed UAVs by benchmarking three camera-based mapping algorithms—DSO, SDSO, and DSOL—in a high-fidelity simulated environment created by AirSim integrated with Cesium tiles. The study demonstrates a robust methodology using a synthetic ground-truth benchmark to analyze geometric accuracy and computational cost, revealing that DSOL best suits resource-constrained UAVs, SDSO excels with modest compute resources, and DSO offers dense mapping with a single camera. The contributions include a comprehensive algorithm comparison, a realistic simulation-based benchmark, and practical sensor recommendations for low-altitude, high-speed UAV mapping, with ground-truth-based evaluation via ICP alignment and mean-±std error metrics. The findings provide actionable guidance for selecting mapping pipelines tailored to UAV hardware constraints and mission requirements, enabling more reliable real-time 3D mapping in dynamic aerial contexts.

Abstract

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.

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

TL;DR

This work tackles the challenge of mapping with low-altitude, high-speed UAVs by benchmarking three camera-based mapping algorithms—DSO, SDSO, and DSOL—in a high-fidelity simulated environment created by AirSim integrated with Cesium tiles. The study demonstrates a robust methodology using a synthetic ground-truth benchmark to analyze geometric accuracy and computational cost, revealing that DSOL best suits resource-constrained UAVs, SDSO excels with modest compute resources, and DSO offers dense mapping with a single camera. The contributions include a comprehensive algorithm comparison, a realistic simulation-based benchmark, and practical sensor recommendations for low-altitude, high-speed UAV mapping, with ground-truth-based evaluation via ICP alignment and mean-±std error metrics. The findings provide actionable guidance for selecting mapping pipelines tailored to UAV hardware constraints and mission requirements, enabling more reliable real-time 3D mapping in dynamic aerial contexts.

Abstract

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.
Paper Structure (32 sections, 6 equations, 17 figures, 2 tables)

This paper contains 32 sections, 6 equations, 17 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Structure-from-Motion vs. Stereo Reconstruction
  4. Structure-from-Motion
  5. Stereo Reconstruction
  6. Mapping Algorithms
  7. DSO: Direct Sparse Odometry
  8. SDSO: Stereo Direct Sparse Odometry
  9. DSOL: Direct Sparse Odometry Lite
  10. Aerial Simulation Solutions
  11. Methodology
  12. Sensors for UAV Mapping
  13. LiDAR
  14. Event Cameras
  15. Electro-Optical and Infrared Cameras
  16. ...and 17 more sections

Figures (17)

  • Figure 1: (a) Epipolar geometry of two cameras. (b) Epipolar geometry of a rectified image pair.
  • Figure 2: The dependency between depth estimation accuracy and the baseline of the stereo camera design for a baseline, $B$ of 34 $cm$, based on irmisch2017camera.
  • Figure 3: Several LiDAR sensors were evaluated for inclusion on the platform. Left to right are shown (a) the Ouster OS1, (b) the HRL131, (c) the RIEGL miniVUX-HA, and (d) the L3 Harris Tactical Geiger-Mode LiDAR sensors.
  • Figure 4: A collection of event cameras commercially available from the iniVation Corp eventcameras.
  • Figure 5: An example of an AirSim city environment showing the following: (a) the FPV view in the simulator where the drone is hovering, (b) RGB image from the simulated left camera mounted on the drone, (c) RGB image from the simulated right camera, and (d) depth image from the simulated depth sensor where objects closer to the depth camera appear darker.
  • ...and 12 more figures