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High-speed multi-dimensional relative navigation for uncooperative space objects
This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-the-art 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation
RPNet: an End-to-End Network for Relative Camera Pose Estimation
This paper addresses the task of relative camera pose estimation from raw
image pixels, by means of deep neural networks. The proposed RPNet network
takes pairs of images as input and directly infers the relative poses, without
the need of camera intrinsic/extrinsic. While state-of-the-art systems based on
SIFT + RANSAC, are able to recover the translation vector only up to scale,
RPNet is trained to produce the full translation vector, in an end-to-end way.
Experimental results on the Cambridge Landmark dataset show very promising
results regarding the recovery of the full translation vector. They also show
that RPNet produces more accurate and more stable results than traditional
approaches, especially for hard images (repetitive textures, textureless
images, etc). To the best of our knowledge, RPNet is the first attempt to
recover full translation vectors in relative pose estimation
Learning Articulated Motions From Visual Demonstration
Many functional elements of human homes and workplaces consist of rigid
components which are connected through one or more sliding or rotating
linkages. Examples include doors and drawers of cabinets and appliances;
laptops; and swivel office chairs. A robotic mobile manipulator would benefit
from the ability to acquire kinematic models of such objects from observation.
This paper describes a method by which a robot can acquire an object model by
capturing depth imagery of the object as a human moves it through its range of
motion. We envision that in future, a machine newly introduced to an
environment could be shown by its human user the articulated objects particular
to that environment, inferring from these "visual demonstrations" enough
information to actuate each object independently of the user.
Our method employs sparse (markerless) feature tracking, motion segmentation,
component pose estimation, and articulation learning; it does not require prior
object models. Using the method, a robot can observe an object being exercised,
infer a kinematic model incorporating rigid, prismatic and revolute joints,
then use the model to predict the object's motion from a novel vantage point.
We evaluate the method's performance, and compare it to that of a previously
published technique, for a variety of household objects.Comment: Published in Robotics: Science and Systems X, Berkeley, CA. ISBN:
978-0-9923747-0-
On Using Gait Biometrics to Enhance Face Pose Estimation
Many face biometrics systems use controlled environments where subjects are viewed directly facing the camera. This is less likely to occur in surveillance environments, so a process is required to handle the pose variation of the human head, change in illumination, and low frame rate of input image sequences. This has been achieved using scale invariant features and 3D models to determine the pose of the human subject. Then, a gait trajectory model is generated to obtain the correct the face region whilst handing the looming effect. In this way, we describe a new approach aimed to estimate accurate face pose. The contributions of this research include the construction of a 3D model for pose estimation from planar imagery and the first use of gait information to enhance the face pose estimation process
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