On-Manifold Preintegration for Real-Time Visual--Inertial Odometry

Current approaches for visual-inertial odometry (VIO) are able to attain highly accurate state estimation via nonlinear optimization. However, real-time optimization quickly becomes infeasible as the trajectory grows over time; this problem is further emphasized by the fact that inertial measurements come at high rate, hence, leading to the fast growth of the number of variables in the optimization. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes into single relative motion constraints. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group. We formally discuss the generative measurement…

• NS
  National Science Foundation

  Award: 11115678
• UD
  U.S. Department of Agriculture
• UZ
  Universität Zürich
• NI
  National Institute of Food and Agriculture

  Award: GEOW-2014-09160