esa pose estimation challenge

Including a separate detection step was also found to be an important element of high performing pose estimation pipelines. The normalized position error, ¯et is also defined as. and validate the developed algorithms. “The grayscale images show a variety of different attitudes and distances – from between 5 to 40 m away – and different lighting conditions. Otherwise a solution to the optimization problem might not be relevant to the original scientific problem. The distance of the satellite in the synthetic images is between 3 and 40.5 meters. “The two PRISMA small satellites, Tango and Mango, took multiple photos of one another over the course of the mission,” says Dario Izzo of ESA’s Advanced Concepts Team, overseeing the competition. 07/07/2017 ∙ by Shanxin Yuan, et al. available. Imagery, https://kelvins.esa.int/satellite-pose-estimation-challenge/data/, https://gitlab.com/EuropeanSpaceAgency/speed-utils, https://kelvins.esa.int/satellite-pose-estimation-challenge/results/. The resulting models were used for generating bounding box or segmentation ground truth from the available pose labels, and in some cases directly in the pose estimation process with PnP solvers. The dataset for the SPEC, named Spacecraft Pose Estimation Dataset (SPEED), mostly consists of synthetic images and the submissions were solely ranked by their accuracy as evaluated on these images. While there is a plethora of large-scale datasets for various terrestrial applications of computer vision and pose estimation that allows training the state-of-the-art machine learning models, there is a lack of such datasets for spacecraft pose estimation. “But new eras come with their challenges, and methods to solve them. ESA’s Advanced Concepts Team (ACT) has teamed up with Stanford University’s Space Rendezvous Laboratory for its latest competition to harness machine learning for space-related goals. On the other hand, an analysis of the submissions and comparison of the efficacy of different approaches are presented based on a survey conducted among the participants. The teams addressed the pose estimation problem as a regression task, except for one team that framed orientation prediction as a soft classification problem. Stanford SLAB + ESA ACT pose estimation dataset and challenge. enabling future on-orbit servicing and debris removal missions. Specifically, the performances of the top 10 teams were analyzed to compare the PnP solutions and strong direct pose estimation submissions. Most of the teams (except for three) consisted of a single individual contributor, affiliated with academic institutions (35%) or industry (30%). This is expected, since larger target distance results in a smaller apparent size of the satellite, corresponding to less pixels associated with the spacecraft. Having a cluttered Earth background makes the pose estimation more difficult. Specifically, a number of top-scoring teams used a separate CNN to perform localization before cropping in order to prevent any loss of information due to downscaling. Upon successful extraction of said features, iterative algorithms are required to predict the best pose solution that minimizes a certain error criterion in the presence of outliers and unknown features correspondences. 6 shows the range of relative position distributions in the dataset in the camera frame. The main reason arises from the difficulty of acquiring thousands of spaceborne images of the desired target spacecraft with accurately annotated pose labels. However, the capability to crop irrelevant parts and zoom in on the important part of the image makes a significant difference in orientation estimation. The disadvantages of this approach are the added complexity and the need for segmentation/bounding box annotation via a separate model reconstruction step. ∙ The general trend is that the images with black background, representing the case of an under-exposed star field, are easier compared to the samples with Earth background. the European Space Agency (ESA). ESA’s Advanced Concepts Team (ACT) has teamed up with Stanford University’s Space Rendezvous Laboratory for its latest competition to harness machine learning for space-related goals. Some have a realistic Earth in the background, others have the blackness of space. SPEED represents the first publicly available machine learning data set for spacecraft pose estimation.222https://kelvins.esa.int/satellite-pose-estimation-challenge/data/ The images of the Tango spacecraft from the PRISMA mission [damico_benn_jorgensen_2014, PRISMA_chapter] are generated from two different sources, referred to as synthetic and real images in the following. While it is not uncommon to have separate orientation and position metrics [Kendall2015_PoseNet], a single scalar score was used instead to rank the submissions on the leaderboard. Deep learning approaches dominated the submissions, as all teams used deep learning either in an end-to-end fashion or as an intermediary process in their pipelines. The analysis on the submissions discovered that the target distance and cluttered backgrounds are the most significant factors contributing to the difficulty of samples. A main concern during the creation of the competition metric was to balance its sensitivity to position and orientation errors and avoid situations where one factor dominates the metric while neglecting the other. Satellite Pose Estimation Challenge aims at evaluating and comparing monocular Furthermore, the Since the 3D model for the satellite was not released as part of the competition, some teams chose to reconstruct the satellite model in order to use any keypoints-based architecture. Failed to retrieve activity summary data. Fig. The first 20 teams significantly outperformed the initial baseline with the top teams getting a two orders of magnitude improvement over the baseline solutions.555Final leaderboard: https://kelvins.esa.int/satellite-pose-estimation-challenge/results/, Best results for each metric are highlighted with bold fonts. Join one of the world's largest A.I. SPEC particularly aimed to focus community efforts on the problem of estimating pose of uncooperative satellites. FAIR-SPACE Hub Director: Professor Sir Martin Sweeting, fairspacehub@surrey.ac.uk       +44 (0)1483 682272, © FAIR-SPACE, C/O University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom, Images used on this website are courtesy of NASA and FAIR-SPACE partners. It represents a common situation in spaceborne applications in which the images of an orbiting satellite are scarce and difficult to obtain. 5 graphically describes the relevant reference frames to compute the errors. Fig. We propose an approach to estimate the 6DOF pose of a satellite, relativ... In this situation, the apparent size of the satellite can be comparable with features on the background image, and in some cases the contrast of the satellite to the background is minimal. In particular, it discusses how keypoint matching techniques compare to pure deep learning approaches and what the effect of a separate localization step is in the pose estimation pipeline. As part of the challenge, participants were tasked with estimating the pose of the Tango spacecraft from its synthetic and real images captured using computer graphics and a robotic testbed, respectively. The keypoints generally correspond to the corners of the satellite body and the tips of the antennae. share, We propose an image-based cross-view geolocalization method that estimat... Note that since the position error is dependent on the target distance, the balance between the two contributions also depends on the particular distance distribution of the test set. This is followed by an in-depth analysis of the final submissions in Section V. Finally, the recommendations for further improvements are given in Section VI. Northern Kenya, The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation, Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using

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