Integrating IMU and landmark sensors for 3D SLAM and the observability analysis

This paper investigates 3-dimensional Simultaneous Localization and Mapping (SLAM) and the corresponding observability analysis by fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF). In addition to the vehicle's states and landmark positions, the self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. Examining the observability of the 3D SLAM system leads to the the conclusion that the system remains observable provided that at least one of these conditions is satisfied i) two known landmarks of which the connecting line is not collinear with the vector of the acceleration are observed ii) three known landmarks which are not placed in a straight line are observed.