TY - GEN
T1 - Modeling the kinematics of an Autonomous Underwater Vehicle for range-bearing simultaneous localization and Mapping
AU - Matsebe, O.
AU - Holtzhausen, S.
AU - Kumile, C. M.
AU - Tlale, N. S.
PY - 2008
Y1 - 2008
N2 - The "solution" of the Simultaneous Localisation and Mapping (SLAM) problem has been one of the notable successes of the robotics community. SLAM has been formulated and solved as a theoretical problem in a number of different forms. SLAM has also been implemented in a number of different domains from indoor robots to outdoor, underwater, and airborne systems. At a theoretical and conceptual level, SLAM can now be considered a solved problem. However, substantial issues remain in practically realizing more general SLAM solutions and notably in building and using perceptually rich maps as part of a SLAM algorithm. This paper describes the Autonomous Underwater Vehicle (AUV) kinematic and sensor models, it overviews the basic theoretical solution to the Extended Kalman Filter (EKF) SLAM problem, it also describes the way-point guidance based on Line of Sight (LOS). In this paper, it has been shown through Matlab simulation that the vehicle is able to localize its position using features that it observes in the environment and at the same time map those features. The vehicle is expected to follow a pre-defined sinusoidal path.
AB - The "solution" of the Simultaneous Localisation and Mapping (SLAM) problem has been one of the notable successes of the robotics community. SLAM has been formulated and solved as a theoretical problem in a number of different forms. SLAM has also been implemented in a number of different domains from indoor robots to outdoor, underwater, and airborne systems. At a theoretical and conceptual level, SLAM can now be considered a solved problem. However, substantial issues remain in practically realizing more general SLAM solutions and notably in building and using perceptually rich maps as part of a SLAM algorithm. This paper describes the Autonomous Underwater Vehicle (AUV) kinematic and sensor models, it overviews the basic theoretical solution to the Extended Kalman Filter (EKF) SLAM problem, it also describes the way-point guidance based on Line of Sight (LOS). In this paper, it has been shown through Matlab simulation that the vehicle is able to localize its position using features that it observes in the environment and at the same time map those features. The vehicle is expected to follow a pre-defined sinusoidal path.
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U2 - 10.1109/MMVIP.2008.4749569
DO - 10.1109/MMVIP.2008.4749569
M3 - Conference contribution
AN - SCOPUS:61949479706
SN - 9780473135324
T3 - 15th International Conference on Mechatronics and Machine Vision in Practice, M2VIP'08
SP - 412
EP - 417
BT - 15th International Conference on Mechatronics and Machine Vision in Practice, M2VIP'08
T2 - 15th International Conference on Mechatronics and Machine Vision in Practice, M2VIP'08
Y2 - 2 December 2008 through 4 December 2008
ER -