publication . Conference object . Other literature type . 2015

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

Frank Dellaert; Luca Carlone; Christian Forster; Davide Scaramuzza;
Open Access
  • Published: 01 Jan 2015
  • Publisher: Robotics: Science and Systems (RSS), Rome, 2015.,Robotics: Science and Systems (RSS), Rome, 2015.
  • Country: Switzerland
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 u...
Subjects
free text keywords: Department of Informatics, 000 Computer science, knowledge & systems, Inertial measurement unit, Computer vision, Computer science, Filter (signal processing), Computational complexity theory, Factor graph, Inertial frame of reference, Propagation of uncertainty, Artificial intelligence, business.industry, business, Maximum a posteriori estimation, Linearization
Funded by
SNSF| Swarm of Flying Cameras
Project
  • Funder: Swiss National Science Foundation (SNSF)
  • Project Code: 200021_143607
  • Funding stream: Project funding | Project funding (Div. I-III)
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