3D SLAM using IMU and its 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 the line connecting the two known landmarks is not collinear with the vector of total acceleration, i.e., the sum of gravitational and inertial accelerations.

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