# A Multi-Sensor Fusion Approach for Rapid Orthoimage Generation in Large-Scale UAV Mapping

arXiv: https://arxiv.org/abs/2503.01202 · HTML: https://mlsanity.com/paper/2503.01202/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 48 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Global Structure-from-Motion Revisited](https://mlsanity.com/paper/2407.20219/) (2024)
- [4D Millimeter-Wave Radar in Autonomous Driving: A Survey](https://mlsanity.com/paper/2306.04242/) (2023)
- [Radar SLAM: A Robust SLAM System for All Weather Conditions](https://mlsanity.com/paper/2104.05347/) (2021)

## Built on by (most cited first, 1 of 1)

- [High-Quality Spatial Reconstruction and Orthoimage Generation Using Efficient 2D Gaussian Splatting](https://mlsanity.com/paper/2503.19703/) (2025)

## Abstract (arXiv)

Rapid generation of large-scale orthoimages from Unmanned Aerial Vehicles (UAVs) has been a long-standing focus of research in the field of aerial mapping. A multi-sensor UAV system, integrating the Global Positioning System (GPS), Inertial Measurement Unit (IMU), 4D millimeter-wave radar and camera, can provide an effective solution to this problem. In this paper, we utilize multi-sensor data to overcome the limitations of conventional orthoimage generation methods in terms of temporal performance, system robustness, and geographic reference accuracy. A prior-pose-optimized feature matching method is introduced to enhance matching speed and accuracy, reducing the number of required features and providing precise references for the Structure from Motion (SfM) process. The proposed method exhibits robustness in low-texture scenes like farmlands, where feature matching is difficult. Experiments show that our approach achieves accurate feature matching orthoimage generation in a short time. The proposed drone system effectively aids in farmland detection and management.