Continuous-Time Spline Visual-Inertial Odometry

TL;DR

This paper introduces a continuous-time spline-based approach for visual-inertial odometry that models poses as cubic splines, enabling accurate and efficient state estimation even with sensor synchronization issues.

Contribution

The paper presents a novel continuous-time spline formulation for VIO that effectively handles rolling shutter effects and sensor asynchrony, achieving state-of-the-art accuracy.

Findings

Achieves state-of-the-art accuracy on public datasets.

Demonstrates real-time computational efficiency.

Effectively addresses rolling shutter distortion.

Abstract

We propose a continuous-time spline-based formulation for visual-inertial odometry (VIO). Specifically, we model the poses as a cubic spline, whose temporal derivatives are used to synthesize linear acceleration and angular velocity, which are compared to the measurements from the inertial measurement unit (IMU) for optimal state estimation. The spline boundary conditions create constraints between the camera and the IMU, with which we formulate VIO as a constrained nonlinear optimization problem. Continuous-time pose representation makes it possible to address many VIO challenges, e.g., rolling shutter distortion and sensors that may lack synchronization. We conduct experiments on two publicly available datasets that demonstrate the state-of-the-art accuracy and real-time computational efficiency of our method.