Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Kalman filtering: with real-time applications (2nd ed.)
Kalman filtering: with real-time applications (2nd ed.)
Real-time implementation of airborne inertial-SLAM
Robotics and Autonomous Systems
3D Position Tracking in Challenging Terrain
International Journal of Robotics Research
Autonomous Stair Climbing for Tracked Vehicles
International Journal of Robotics Research
Journal of Intelligent and Robotic Systems
Attitude determination and localization of mobile robots using two RTK GPSs and IMU
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
The Effects of Partial Observability When Building Fully Correlated Maps
IEEE Transactions on Robotics
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This paper investigates 3-dimensional (3D) 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. The discrete-time covariance matrix of the process noise, the state transition matrix and the observation sensitivity matrix are derived in closed form, making it suitable for real-time implementation. Examination of the observability of the 3D SLAM system leads to the the conclusion that the system remains observable, provided that at least three known landmarks, which are not placed in a straight line, are observed.