Keyframe-Based RGB-D Visual-Inertial Odometry and Camera Extrinsic Calibration Using Extended Kalman Filter

This paper proposes a novel method for Visual-Inertial Odometry (VIO) based on a RGB-D camera and an Inertial Measurement Unit (IMU). To fuse the data from visual and inertial measurements, an Extended Kalman Filter (EKF) is deployed, while we propose an iterative procedure to reduce the linearization error. Our method not only estimates the trajectory of the camera, but also calibrates the gravity and the camera extrinsics, i.e. the relative pose between camera and IMU. For what concerns visual odometry, a KeyFrame strategy is employed, as it tends to outperform frame-to-frame alignment. Moreover, we combine feature- and ICP-based visual odometry, so to provide accurate and robust estimation. Evaluation results on simulation and real data show the effectiveness of our approach.