Abstract
This research deals with mobile robot SLAM algorithm based on extended kalman filter. To enhance a accuracy of robot pose, one more extended kalman filter is used in a rough surface environment. The robot has uncertain kinematic model due to a caterpillar. When the robot drives on irregular surface, it's heading can be corrupted. We propose a method to correct uncertain robot pose using one more extended kalman filter through simulation results.
Highlights
It is a basic and essential ability to recognize exactly unknown environment for intelligent autonomous mobile robot system
The robot implements SLAM based on the extended kalman filter, one more extended kalman fiter is used to supplment uncertain robot pose due to unreliable robot’s kinematic property and to correct rocking robot’s heading because of irregular driving surface
This paper introduced supplmentary Extended Kalman Filter (EKF) application to correct robot’s heading that is corrupted when the caterpillar type robot drives on a rough surface road
Summary
It is a basic and essential ability to recognize exactly unknown environment for intelligent autonomous mobile robot system. A robot that moves with constant translational and rotational velocity typically moves on a circular trajectory, which can not be described by linear state transitions This observation, along with the assumption of unimodal beliefs, renders. The extended kalman filter is a recursive estimator This means that only the estimated state from the previous time step and the current measurement are needed to compute the estimate for the current state. SLAM (Simultaneous Localization and Mapping) The dominant approach to the SLAM problem was introduced in a seminal paper by Smith and Cheeseman in 1986, and first developed into an implemented system by Moutarlier and Chatila This approach uses the Extended Kalman Filter (EKF) to estimate the posterior over robot pose and maps.
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