Browsing by Author "Rondao, Duarte"

Now showing 1 - 5 of 5
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    Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space
    (Elsevier, 2020-04-07) Rondao, Duarte; Aouf, Nabil; Richardson, Mark A.; Dubois-Matra, Olivier
    Optical-based navigation for space is a field growing in popularity due to the appeal of efficient techniques such as Visual Simultaneous Localisation and Mapping (VSLAM), which rely on automatic feature tracking with low-cost hardware. However, low-level image processing algorithms have traditionally been measured and tested for ground-based exploration scenarios. This paper aims to fill the gap in the literature by analysing state-of-the-art local feature detectors and descriptors with a taylor-made synthetic dataset emulating a Non-Cooperative Rendezvous (NCRV) with a complex spacecraft, featuring variations in illumination, rotation, and scale. Furthermore, the performance of the algorithms on the Long Wavelength Infrared (LWIR) is investigated as a possible solution to the challenges inherent to on-orbit imaging in the visible, such as diffuse light scattering and eclipse conditions. The Harris, GFTT, DoG, Fast-Hessian, FAST, CenSurE detectors and the SIFT, SURF, LIOP, ORB, BRISK, FREAK descriptors are benchmarked for images of Envisat. It was found that a combination of Fast-Hessian with BRISK was the most robust, while still capable of running on a low resolution and acquisition rate setup. For large baselines, the rate of false-positives increases, limiting their use in model-based strategies.
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    ChiNet: deep recurrent convolutional learning for multimodal spacecraft pose estimation
    (IEEE, 2022-07-22) Rondao, Duarte; Aouf, Nabil; Richardson, Mark A.
    This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM) units in modelling sequences of data for the processing of features extracted by a convolutional neural network (CNN) backbone. Three distinct training strategies, which follow a coarse-to-fine funnelled approach, are combined to facilitate feature learning and improve end-to-end pose estimation by regression. The capability of CNNs to autonomously ascertain feature representations from images is exploited to fuse thermal infrared data with electro-optical red-green-blue (RGB) inputs, thus mitigating the effects of artifacts from imaging space objects in the visible wavelength. Each contribution of the proposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, and the complete pipeline is validated on experimental data.
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    Multi-view monocular pose estimation for spacecraft relative navigation
    (AIAA, 2018-01-07) Rondao, Duarte; Aouf, Nabil
    This paper presents a method of estimating the pose of a non-cooperative target for spacecraft rendezvous applications employing exclusively a monocular camera and a threedimensional model of the target. This model is used to build an offline database of prerendered keyframes with known poses. An online stage solves the model-to-image registration problem by matching two-dimensional point and edge features from the camera to the database. We apply our method to retrieve the motion of the now inoperational satellite ENVISAT. The combination of both feature types is shown to produce a robust pose solution even for large displacements respective to the keyframes which does not rely on real-time rendering, making it attractive for autonomous systems applications.
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    Multispectral image processing for navigation using low performance computing
    (International Astronautical Federation (IAF), 2018-10) Rondao, Duarte; Aouf, Nabil; Dubois-Matra, Olivier
    Space debris represents a growing threat for both current spacecraft and future launches. This is exceptionally alarming in the case of low Earth orbits, where chain impacts of existing debris generate even more fragments, increasing the probability of further collisions. The now defunct satellite Envisat represents one of the largest objects classified as space debris. The e.Deorbit mission will demonstrate active debris removal (ADR) technology to successfully decommission Envisat and other non-functional target spacecraft in orbit. Relative navigation solutions shall be achieved using image processing algorithms, which implies the detection and matching of two-dimensional regions of interest. In this work, multiple pattern recognition techniques are investigated for the detection and description of these features. This analysis of feature perception is achieved for the first time in the context of space non-cooperative rendezvous (NCRV) across two different modalities: the visible (0.39-0.70 μm) and the thermal infrared (8-14 μm). The assessed algorithms are implemented in a dedicated, space-appropriate hardware processor to benchmark their real-time capabilities.
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    Robust on-manifold optimization for uncooperative space relative navigation with a single camera
    (AIAA, 2021-03-31) Rondao, Duarte; Aouf, Nabil; Richardson, Mark A.; Dubanchet, Vincent
    Optical cameras are gaining popularity as the suitable sensor for relative navigation in space due to their attractive sizing, power, and cost properties when compared with conventional flight hardware or costly laser-based systems. However, a camera cannot infer depth information on its own, which is often solved by introducing complementary sensors or a second camera. In this paper, an innovative model-based approach is demonstrated to estimate the six-dimensional pose of a target relative to the chaser spacecraft using solely a monocular setup. The observed facet of the target is tackled as a classification problem, where the three-dimensional shape is learned offline using Gaussian mixture modeling. The estimate is refined by minimizing two different robust loss functions based on local feature correspondences. The resulting pseudomeasurements are processed and fused with an extended Kalman filter. The entire optimization framework is designed to operate directly on the SE(3) manifold, uncoupling the process and measurement models from the global attitude state representation. It is validated on realistic synthetic and laboratory datasets of a rendezvous trajectory with the complex spacecraft Envisat, demonstrating estimation of the relative pose with high accuracy over full tumbling motion. Further evaluation is performed on the open-source SPEED dataset.