Drone Localization using Global Navigation Satellite System and Separated Feature Visual Odometry Data Fusion
Received: 29 September 2024 | Revised: 22 October 2024 | Accepted: 23 November 2024 | Online: 2 February 2025
Corresponding author: Riza Agung Firmansyah
Abstract
The localization system is the most important part of the overall drone navigation system. The Global Positioning System (GPS) or Global Navigation Satellite System (GNSS) is the main device commonly used in a drone. However, under certain conditions, GPS or GNSS may not function optimally, such as in situations of signal jamming or enclosed environments. This paper implemented a new approach to address this issue by combining GNSS data with Visual Odometry (VO) through Machine Learning (ML) methods. The followed process consists of three main stages. First, performing speed and orientation estimation using VO. Second, performing left and right feature separation on the images to generate a more stable and robust estimation of speed and rotation. Third, refining speed and orientation estimation by integrating GNSS data through ML-based data fusion. The proposed method strives to enhance drone localization accuracy, despite disruptions or unavailability of GNSS signals. The research results indicate that the introduced method significantly reduces Absolute Translation Error (ATE) compared to utilizing VO or GNSS separately. The average ATE produced reached 4.38 m and an orientation of 8.26°, indicating that this data fusion approach provides a significant improvement in drone localization accuracy, making it reliable in operational scenarios with limited GNSS signals.
Keywords:
drone localization, data fusion, machine learning, visual odometry, GNSSDownloads
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Copyright (c) 2024 Riza Agung Firmansyah, Syahri Muharom, Ilmiatul Masfufiah, Ardylan Heri Kisyarangga, Dzichril Fahimatulloh Mandhia Al Farizi Rosyad
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