CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations

Abstract

The estimation error accumulation in the conventional visual inertial odometry (VIO) generally forbids accurate long-term operations. Some advanced techniques such as global pose graph optimization and loop closure demand relatively high computation and processing time to execute the optimization procedure for the entire trajectory and may not be feasible to be implemented in a low-cost robotic platform. In an attempt to allow the VIO to operate for a longer duration without either using or generating a map, this paper develops iterated cubature Kalman filter for VIO application that performs multiple corrections on a single measurement to optimize the current filter state and covariance during the measurement update. The optimization process is terminated using the maximum likelihood estimate based criteria. For comparison, this paper also develops a second solution to integrate VIO estimation with ranging measurements. The wireless communications between the vehicle and multiple beacons produce the ranging measurements and help to bound the accumulative errors. Experiments utilize publicly available dataset for validation, and a rigorous comparison between the two solutions is presented to determine the application scenario of each solution.