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京东 11.11 红包
Equivariant Observers: Robust Nonlinear State Estimation for Robotic Systems
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Equivariant Observers: Robust Nonlinear State Estimation for Robotic Systems by Robert Mahony @CDC2019 IEEE CSS 官网没有@CDC2019视频,在别处找了类似的替代(An Equivariant Perspective on Spatial Awareness)。 Abstract: The physical state of a robotic system naturally carries structure; the pose of rigid links can be written as elements of the Special Euclidean group, images taken by a camera of a planar scene can be related by homographies and mapped to elements of the special linear group, etc. Recent work has demonstrated that there is a rich collection of symmetry groups for different robotic problems above and beyond the classical Lie-groups. This talks shows how this structure can be exploited to design robust nonlinear observers for state estimation. The earliest results in this direction were nonlinear attitude estimators (2005-2010) that were an enabling technology in the aerial robotic vehicle industry. Pose estimation algorithms based on these ideas are built into the augmented reality headsets that are now ubiquitous in gaming. Recent symmetries have opened the door to new solutions for classical robotics problems such as visual odometry, visual inertial odometry, simultaneous localisation and mapping. Biography: Robert Mahony is a Professor in the Research School of Engineering at the Australian National University. He received his BSc in 1989 (applied mathematics and geology) and his PhD in 1995 (systems engineering) both from the Australian National University. He is a fellow of the IEEE and was president of the Australian Robotics Association from 2008-2011. He was Director of the Research School of Engineering at the Australian National University 2014-2016. His research interests are in non-linear systems theory with applications in robotics and computer vision. He is known for his work in aerial robotics, equivariant observer design, matrix subspace optimisation and image based visual servo control
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