A Multi-Sensor Fusion Approach for Rapid Orthoimage Generation in Large-Scale UAV Mapping
This work addresses the need for efficient aerial mapping in agriculture, specifically for farmland detection and management, though it appears incremental as it builds on existing multi-sensor and feature matching techniques.
The paper tackles the problem of rapid orthoimage generation for large-scale UAV mapping by introducing a multi-sensor fusion approach that integrates GPS, IMU, radar, and camera data, resulting in accurate feature matching and orthoimage generation in a short time with robustness in low-texture scenes like farmlands.
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.