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.
|Original language||English (US)|
|Title of host publication||2022 IEEE International Conference on Robotics and Automation, ICRA 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|State||Published - 2022|
|Event||39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States|
Duration: May 23 2022 → May 27 2022
|Name||2022 International Conference on Robotics and Automation (ICRA)|
|Conference||39th IEEE International Conference on Robotics and Automation, ICRA 2022|
|Period||5/23/22 → 5/27/22|
Bibliographical noteFunding Information:
This work was partially supported by the Minnesota Robotics Institute Seed (MnRI) Grant and the National Science Foundation award IIS-#1637875.
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