Work

Research on an Improved Stepwise Feature Matching Algorithm for UAV Indoor Localization

Year: 2025

Type: article

Abstract: Indoor unmanned aerial vehicles (UAVs) localization faces challenges such as insufficient feature matches, high false match rates, and low accuracy under low-light conditions, viewpoint changes, and occlusions. To address these issues, we propose an adaptive FAST (Features from Accelerated Segment Test) corner detection thresholding method that adapts to indoor lighting and texture variations. Building upon the FAPP algorithm, our approach optimizes threshold selection during fine matching when correct and incorrect matching pairs are unevenly distributed. The proposed algorithm demonstrates superior robustness and efficiency in feature matching. In actual indoor UAV flight experiments, the feature matching rate reached 96.5%, and the indoor localization accuracy reached 0.02 meters. Compared to existing literature, both the average correct matching rate and localization accuracy were significantly improved, effectively solving the problem of precise indoor UAV localization.

Source: IEEE Access

DOI: 10.1109/access.2025.3559989

Authors: Yong He, Xiaochuan He

Institution: Changsha University of Science and Technology

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075