Continuous-Time Spline Visual-Inertial Odometry
This work improves VIO for robotics and autonomous systems by offering a novel method to handle common sensor issues, though it is incremental in its approach.
The paper tackles the problem of visual-inertial odometry (VIO) by proposing a continuous-time spline-based formulation that models poses as cubic splines to synthesize IMU measurements, addressing challenges like rolling shutter distortion and sensor synchronization. Experiments on two datasets show state-of-the-art accuracy and real-time computational efficiency.
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.