  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.
