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Unscented Particle Filters with Refinement Steps for UAV Pose Tracking

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Abstract

Particle Filters (PFs) have been successfully employed for monocular 3D model-based tracking of rigid objects. However, these filters depend on the computation of importance weighs that use sub-optimal approximations to the likelihood function. In this paper, we propose to enrich the filter with additional refinement steps to abridge its sub-optimality. We test the proposed approach in two different types of PFs: (i) an Unscented Particle Filter (UPF), and (ii) the recently proposed Unscented Bingham Filter (UBiF). These filters are applied to the outdoor tracking of a fixed-wing Unmanned Aerial Vehicle (UAV) autonomous landing in a Fast Patrol Boat (FPB), tested in a simulated environment with a real sky gradient filled with clouds. The use of the refinement steps significantly improves the overall accuracy of the method.

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Authors and Affiliations

  1. Institute for Systems and Robotics (ISR), University of Lisbon, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal

    Nuno Pessanha Santos & Alexandre Bernardino

  2. Portuguese Navy Research Center (CINAV), Escola Naval, Base Naval de Lisboa, 2810-001, Almada, Portugal

    Nuno Pessanha Santos & Victor Lobo

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  1. Nuno Pessanha Santos
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  2. Victor Lobo
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  3. Alexandre Bernardino
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The first author entirely performed this work during his Ph.D. studies and was guided by the second and third authors.

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Correspondence to Nuno Pessanha Santos.

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Pessanha Santos, N., Lobo, V. & Bernardino, A. Unscented Particle Filters with Refinement Steps for UAV Pose Tracking. J Intell Robot Syst 102, 52 (2021). https://doi.org/10.1007/s10846-021-01409-y

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