(xC, yC) and (xB, yB) then form a plane perpendicular to zC and zB, respectively, as shown in Fig. The challenge was hosted on the ACT’s Kelvins competition website. 9 illustrates the final scores. It is possible that the reason those three teams achieved better results on real imagery is related to its limited pose distribution. These top three solutions were the only submissions to outperform the SLAB baseline. We invite submissions to the BOP Challenge 2020 on model-based 6D object pose estimation. Stanford SLAB + ESA ACT pose estimation dataset and challenge. Therefore, there are two key factors that are considered in setting up the competition: community engagement: The participants and the effort they put into solving the problems are our main resource. Some 80% of them will come with pose data included, which can be used for machine learning. The distribution of scores is correlated with the target distance, i.e., it is harder to estimate the pose of the satellites that are farther away. The normalized position error, ¯et is also defined as. The main reason arises from the difficulty of acquiring thousands of spaceborne images of the desired target spacecraft with accurately annotated pose labels. ∙ The ground-truth pose labels, consisting of the translation vector and a unit quaternion describing the relative orientation of the Tango spacecraft with respect to the camera, are released along with the associated training images. Since the dataset consists of single channel grayscale images, this provided additional freedom for teams for constructing their input. 20 teams, including the top 13 competitors, answered the survey. The distance of the satellite in the synthetic images is between 3 and 40.5 meters. 13 also highlights the importance of the inter-spacecraft distance. choice of dataset reflects one of the unique challenges associated with ∙ In order to check the balance of the sensitivities, the total error E was calculated over the test set for two cases: introducing 1∘ of orientation error in the first case, and adding 0.1 m translation error in the second case. Success made the Moon landings possible, along with the large-scale space station construction that followed. SPEC particularly aimed to focus community efforts on the problem of estimating pose of uncooperative satellites. For these images, the illumination conditions are created to best match those of the background Earth images. Moreover, the servicer cannot rely on the availability of known fiduciary markers on these targets. In fact, the average orientation error and its standard deviation is 0.34∘±0.38∘, while the average position error is 0.09±0.09 m.666In comparison, the winning team UniAdelaide achieved 0.41∘±1.50∘ orientation error and 0.13±0.09 m relative position error. While team UniAdelaide [Chen2019SPEC] won the competition by achieving the highest score on the synthetic test set, EPFL_cvlab achieved the highest accuracy on real images. Both have the disadvantage that the orientation and position sensitivity is dependent on the choice of keypoints, since the slope of orientation error is proportional to the distance of the keypoints from the origin of the target’s body frame. Join one of the world's largest A.I. This year, we provide BlenderProc4BOP, an open-source and light-weight photorealistic (PBR) renderer, and a set of pre-rendered training images.This addition reduces the entry barrier of the challenge for participants working on learning-based RGB and RGB-D solutions. Teams have direct information about how their latest submission compares to their peers, the limits are constantly pushed further, and the competitive aspect brings more motivation for teams to put in effort. 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. 15 Additionally, a stronger third baseline solution, also based on CNN, was developed during the competition by SLAB and is used for comparison purposes. Download (5 GB) New Notebook. The Kelvins 2 From Fig. Using the MSE loss, errors in this direction dominate the loss. available machine learning set of synthetic and real spacecraft imagery. 7 visualizes the relative orientation and position distributions for real images in the satellite body frame. Satellite Pose Estimation Challenge aims at evaluating and comparing monocular ESA and Stanford University are challenging global AI specialists to train software to judge the position and orientation of a drifting satellite with a single glance. In an open scientific competition such as SPEC and other Kelvins competitions, scientific problems are turned into well-formulated mathematical problems that are solved by engaging the broader scientific community and citizen scientists. 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 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. Kaggle Kerneler. Kelvins, the platform which hosts SPEC and many other satellite-related challenges, was designed to provide a seamless experience for the participants. Fig. The process is crucial in providing a good initial pose estimate to the on-board vision-based navigation system, On the other hand, recent years have seen a significant breakthrough in computer vision with the advent of Deep Neural Networks (DNN). Therefore, a broad audience has to be reached to attract many individuals and teams. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. It plots the distribution of the pose score for within 1 m distance bin. Northern Kenya, The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation, Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Even though the domain adaptation was not the main focus of the competition, evaluating the submissions on these images provides an indication of the generalization capability of the proposed algorithms. While most teams simply stacked the same input channel to have RGB input, two teams included masked or filtered versions of the input on the extra channels. It is noteworthy that only half of the teams were involved with space related research, and 65% were not working on pose estimation problems at all. 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. The facility also includes custom Light-Emitting Diode (LED) wall panels which can simulate the diffused illumination conditions due to Earth albedo and a xenon short-arc lamp to simulate collimated sunlight in various orbit regimes. This ‘super pose estimator’ is used as a proxy of how difficult the pose estimation task is on a certain sample. Due to physical limitations of the TRON facility in combination with the size of the satellite mockup, the distance distribution of real images is much more constrained, ranging from 2.8 to 4.7 meters. For instance the outputs are not normalized, or the predicted distance along the camera boresight is typically one order of magnitude larger than all the other output variables. These are a combination of fully digital images and physical photos taken using satellite models in representative lighting conditions as part of his research. However, the performances of the same architectures on real images are relatively poor, as the real images have different statistical distributions from the synthetic images that were used to train the DNNs. Finally, the total error E is the average of the pose errors over all N images of the test set. The position error, et, is defined as, the magnitude (2-norm) of difference between the ground-truth (tBC) and estimated (^tBC) position vectors from the origin of the camera reference frame C to that of the target body frame B. The classical approach to monocular-based pose estimation of a target spacecraft [Cropp2002PoseEO, Leinz2008_OrbitalExpress, Zhang2005_pose, Petit2011_CaseStudy, grompone2015_phdthesis, damico_benn_jorgensen_2014, kanani2012] would first extract hand-crafted features of the target from a 2D image. Furthermore, the It especially visualizes the fact that for synthetic images, the relative orientations are well distributed across the 3D space. These baselines allow for incremental improvements, such as replacing the loss function or training on larger input images.
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