Exploiting ground plane constraints for visual-inertial navigation

In this paper, an ego-motion estimation approach is introduced that fuses visual and inertial information, using a monocular camera and an inertial measurement unit. The system maintains a set of feature points that are observed on the ground plane. Based on matched feature points between the current and previous images, a novel measurement model is introduced that imposes visual constraints on the inertial navigation system to perform 6 DoF motion estimation. Furthermore, feature points are used to impose epipolar constraints on the estimated motion between current and past images. Pose estimation is formulated implicitly in a state-space framework and is performed by a Sigma-Point Kalman filter. The presented experiments, conducted in an indoor scenario with real data, indicate the ability of the proposed method to perform accurate 6 DoF pose estimation.