Unscented Particle Filters with Refinement Steps for UAV Pose Tracking

Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing

“…We can use a ground-based approach where the data can be acquired and processed using more advanced methods since we do not usually have tight payload restrictions. Regarding the ground-based applications, we can use stereo vision with a Pan-Tilt Unit (PTU) [83], multi-camera stereo vision with PTUs [84], multiple IR cameras [85], using passive Radio Frequency (RF) reflectors on the runaway [86], or using a monocular vision system [41,42]. In this article, we explored a ground-based approach for performing autonomous landing during the daytime on a ship (moving platform) using advanced algorithms, whose processing is impossible on low-power processors.…”

“…The adopted tracking architecture is divided into: (i) Pose Boosting; (ii) Tracking; (iii) Pose optimization [41,42]. In the Pose Boosting stage, we used Machine Learning (ML) algorithms to detect the UAVs on the captured frame and used a pre-trained database to generate representative pose samples [12,43].…”

“…In the Tracking stage, we used a 3D-modelbased tracking approach using the UAV CAD model based on a PF approach [42]. In the Pose optimization stage, since we used a sub-optimal similarity metric to approximate the likelihood function iteratively, we included optimization steps to improve the estimate [41].…”

“…This article presents the following main developments (innovations): (i) the analysis, test, and comparison on the same dataset of two different PF implementations using pose optimization: (a) an Unscented Bingham Filter (UBiF) [41,42,44] and (b) an Unscented Bingham-Gauss Filter (UBiGaF) [42,45]; (ii) the implementation of a new tree-based similarity metric approach to be able to obtain a faster and more accurate weight estimation; (iii) better analysis and evaluation of how the optimization steps can decrease the filter convergence time; (iv) a validation and comparison between methods using a realistic synthetic dataset. In this article, we did not perform the comparison between UBiF and UBiGaF and more traditional methods such as the Unscented Kalman Filter (UKF) for the orientation estimation since, in [41,42], we already made this comparison and showed that these implementations outperform the UKF. As far as we know, there are no publicly available datasets or other ground-based model-based UAV tracking approaches, making the comparison with other state-of-the-art methods impossible.…”

“…As far as we know, there are no publicly available datasets or other ground-based model-based UAV tracking approaches, making the comparison with other state-of-the-art methods impossible. The main developments regarding our previous work are: (i) in [42], a tracking architecture using a UBiGaF without pose optimization implementation, which is explored in this article, was proposed; (ii) in [41], we applied optimization steps to the UBiF that is implemented in this article also to the UBiGaF; (iii) in [43], we proposed a distance transform similarity metric that is modified to a tree-based approach to be able to decrease the processing time without a loss in accuracy.…”