155 research outputs found
Differentiable Rendering for Pose Estimation in Proximity Operations
Differentiable rendering aims to compute the derivative of the image
rendering function with respect to the rendering parameters. This paper
presents a novel algorithm for 6-DoF pose estimation through gradient-based
optimization using a differentiable rendering pipeline. We emphasize two key
contributions: (1) instead of solving the conventional 2D to 3D correspondence
problem and computing reprojection errors, images (rendered using the 3D model)
are compared only in the 2D feature space via sparse 2D feature
correspondences. (2) Instead of an analytical image formation model, we compute
an approximate local gradient of the rendering process through online learning.
The learning data consists of image features extracted from multi-viewpoint
renders at small perturbations in the pose neighborhood. The gradients are
propagated through the rendering pipeline for the 6-DoF pose estimation using
nonlinear least squares. This gradient-based optimization regresses directly
upon the pose parameters by aligning the 3D model to reproduce a reference
image shape. Using representative experiments, we demonstrate the application
of our approach to pose estimation in proximity operations.Comment: AIAA SciTech Forum 2023, 13 pages, 9 figure
6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics
We present a novel technique to estimate the 6D pose of objects from single
images where the 3D geometry of the object is only given approximately and not
as a precise 3D model. To achieve this, we employ a dense 2D-to-3D
correspondence predictor that regresses 3D model coordinates for every pixel.
In addition to the 3D coordinates, our model also estimates the pixel-wise
coordinate error to discard correspondences that are likely wrong. This allows
us to generate multiple 6D pose hypotheses of the object, which we then refine
iteratively using a highly efficient region-based approach. We also introduce a
novel pixel-wise posterior formulation by which we can estimate the probability
for each hypothesis and select the most likely one. As we show in experiments,
our approach is capable of dealing with extreme visual conditions including
overexposure, high contrast, or low signal-to-noise ratio. This makes it a
powerful technique for the particularly challenging task of estimating the pose
of tumbling satellites for in-orbit robotic applications. Our method achieves
state-of-the-art performance on the SPEED+ dataset and has won the SPEC2021
post-mortem competition.Comment: preprin
Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous
Research on developing deep learning techniques for autonomous spacecraft
relative navigation challenges is continuously growing in recent years.
Adopting those techniques offers enhanced performance. However, such approaches
also introduce heightened apprehensions regarding the trustability and security
of such deep learning methods through their susceptibility to adversarial
attacks. In this work, we propose a novel approach for adversarial attack
detection for deep neural network-based relative pose estimation schemes based
on the explainability concept. We develop for an orbital rendezvous scenario an
innovative relative pose estimation technique adopting our proposed
Convolutional Neural Network (CNN), which takes an image from the chaser's
onboard camera and outputs accurately the target's relative position and
rotation. We perturb seamlessly the input images using adversarial attacks that
are generated by the Fast Gradient Sign Method (FGSM). The adversarial attack
detector is then built based on a Long Short Term Memory (LSTM) network which
takes the explainability measure namely SHapley Value from the CNN-based pose
estimator and flags the detection of adversarial attacks when acting.
Simulation results show that the proposed adversarial attack detector achieves
a detection accuracy of 99.21%. Both the deep relative pose estimator and
adversarial attack detector are then tested on real data captured from our
laboratory-designed setup. The experimental results from our
laboratory-designed setup demonstrate that the proposed adversarial attack
detector achieves an average detection accuracy of 96.29%
Instance Segmentation for Feature Recognition on Noncooperative Resident Space Objects
Active debris removal and unmanned on-orbit servicing missions have gained interest in the last few years, along with the possibility to perform them through the use of an autonomous chasing spacecraft. In this work, new resources are proposed to aid the implementation of guidance, navigation, and control algorithms for satellites devoted to the inspection of noncooperative targets before any proximity operation is initiated. In particular, the use of convolutional neural networks (CNN) performing object detection and instance segmentation is proposed, and its effectiveness in recognizing the components and parts of the target satellite is evaluated. Yet, no reliable training images dataset of this kind exists to date. A tailored and publicly available software has been developed to overcome this limitation by generating synthetic images. Computer-aided design models of existing satellites are loaded on a three-dimensional animation software and used to programmatically render images of the objects from different points of view and in different lighting conditions, together with the necessary ground truth labels and masks for each image. The results show how a relatively low number of iterations is sufficient for a CNN trained on such datasets to reach a mean average precision value in line with state-of-the-art performances achieved by CNN in common datasets. An assessment of the performance of the neural network when trained on different conditions is provided. To conclude, the method is tested on real images from the Mission Extension Vehicle-1 on-orbit servicing mission, showing that using only artificially generated images to train the model does not compromise the learning process
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