In this paper, we present a linear-complexity 3D inertial navigation algorithm using both point and plane features observed from an RGBD camera. In particular, we study the system's observability properties, and prove that: (i) When observing a single plane feature of known direction, the IMU gyroscope bias is observable. (ii) By observing a single point feature, as well as a single plane of known direction but not perpendicular to gravity, all degrees of freedom of the IMU-RGBD navigation system become observable, up to global translations. Next, based on the results of the observability analysis, we design a consistency-improved, observability-constrained (OC) extended Kalman filter (EKF)-based estimator for the IMU-RGBD camera navigation system. Finally, we experimentally validate the superiority of our proposed algorithm compared to alternative methods in urban scenes.