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Learning minimal representations of places
Use machine learning to condense visual observations of places into minimal representations that allow for 6dof relative pose estimation.
Place recognition and 6DoF localization has a wide range of applications, whether in robot autonomy, VR/AR or navigation interfaces. Given sensor readings (we focus on images), the goal is to establish position and orientation of a robot/device with respect to a previously recorded map. Recently, this is generally solved with a mixture of machine learning and geometry (NetVLAD, SuperPoint, LF-NET, PoseNet). Our focus in particular will be to solve this problem with a minimal representation.
Place recognition and 6DoF localization has a wide range of applications, whether in robot autonomy, VR/AR or navigation interfaces. Given sensor readings (we focus on images), the goal is to establish position and orientation of a robot/device with respect to a previously recorded map. Recently, this is generally solved with a mixture of machine learning and geometry (NetVLAD, SuperPoint, LF-NET, PoseNet). Our focus in particular will be to solve this problem with a minimal representation.
Given query agent A and map agent B, have B establish a pose of A within its map, with minimal data transmission from A to B. We have a couple of ideas on how to solve this (see our most recent publication on this: https://arxiv.org/abs/1811.10681 ), but you are encouraged to bring your own ideas to the table.
Given query agent A and map agent B, have B establish a pose of A within its map, with minimal data transmission from A to B. We have a couple of ideas on how to solve this (see our most recent publication on this: https://arxiv.org/abs/1811.10681 ), but you are encouraged to bring your own ideas to the table.
Titus Cieslewski ( titus at ifi.uzh.ch ), APPLY VIA EMAIL, ATTACH CV AND TRANSCRIPT (also Bachelor)! Preferred skills: Linux, Python, “Vision Algorithms for Mobile Robots” class or equivalent, TensorFlow/PyTorch or equivalent.
Titus Cieslewski ( titus at ifi.uzh.ch ), APPLY VIA EMAIL, ATTACH CV AND TRANSCRIPT (also Bachelor)! Preferred skills: Linux, Python, “Vision Algorithms for Mobile Robots” class or equivalent, TensorFlow/PyTorch or equivalent.