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

TL;DR

This paper introduces a novel preintegration theory for inertial measurements in visual-inertial odometry, enabling real-time, accurate state estimation by efficiently handling the manifold structure of rotations and integrating into factor graph frameworks.

Contribution

The paper presents a new preintegration theory for IMU data on rotation manifolds and integrates it into a visual-inertial pipeline using factor graphs for real-time performance.

Findings

Achieves real-time, accurate state estimation in VIO.

Outperforms existing methods on real and simulated datasets.

Enables efficient incremental smoothing with structureless visual measurement models.

Abstract

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 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 \emph{preintegration theory} that properly addresses the manifold structure of the rotation group. We formally discuss the generative measurement model as well as the nature of the rotation noise and derive the expression for the \emph{maximum a posteriori} state estimator. Our theoretical development enables the computation of all necessary Jacobians for the optimization and a-posteriori bias correction in analytic form. The second contribution is to show that the preintegrated IMU model can be seamlessly integrated into a visual-inertial pipeline under the unifying framework of factor graphs. This enables the application of incremental-smoothing algorithms and the use of a \emph{structureless} model for visual measurements, which avoids optimizing over the 3D points, further accelerating the computation. We perform an extensive evaluation of our monocular \VIO pipeline on real and simulated datasets. The results confirm that our modelling effort leads to accurate state estimation in real-time, outperforming state-of-the-art approaches.