Title:
IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation

dc.contributor.author Forster, Christian
dc.contributor.author Carlone, Luca
dc.contributor.author Dellaert, Frank
dc.contributor.author Scaramuzza, Davide
dc.contributor.corporatename Georgia Institute of Technology. Institute for Robotics and Intelligent Machines en_US
dc.contributor.corporatename Georgia Institute of Technology. College of Computing en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Interactive Computing en_US
dc.contributor.corporatename University of Zürich. Robotics and Perception Group en_US
dc.date.accessioned 2016-07-29T17:49:42Z
dc.date.available 2016-07-29T17:49:42Z
dc.date.issued 2015
dc.description.abstract Recent results in monocular visual-inertial navigation (VIN) have shown that optimization-based approaches outperform filtering methods in terms of accuracy due to their capability to relinearize past states. However, the improvement comes at the cost of increased computational complexity. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes. The preintegration allows us to accurately summarize hundreds of inertial measurements into a single relative motion constraint. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group and carefully deals with uncertainty propagation. The measurements are integrated in a local frame, which eliminates the need to repeat the integration when the linearization point changes while leaving the opportunity for belated bias corrections. The second contribution is to show that the preintegrated IMU model can be seamlessly integrated in a visual-inertial pipeline under the unifying framework of factor graphs. This enables the use of a structureless model for visual measurements, further accelerating the computation. The third contribution is an extensive evaluation of our monocular VIN pipeline: experimental results confirm that our system is very fast and demonstrates superior accuracy with respect to competitive state-of-the-art filtering and optimization algorithms, including off-the-shelf systems such as Google Tango [1]. en_US
dc.embargo.terms null en_US
dc.identifier.citation Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D. (2015). IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation. Robotics: Science and Systems (RSS), Rome, 2015. en_US
dc.identifier.uri http://hdl.handle.net/1853/55417
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Inertial measurement unit en_US
dc.subject Preintegration en_US
dc.title IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation en_US
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.author Dellaert, Frank
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
relation.isAuthorOfPublication dac80074-d9d8-4358-b6eb-397d95bdc868
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
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