Abstract
In this paper, we present a solution to the Simultaneous Localization and Mapping (SLAM) problem for an indoor robot using bearing-only observations. An omnidirectional camera is used to observe indoor scene from which vertical lines are extracted to obtain bearing measurements. To track vertical lines through sequence of omnidirectional images, a matching algorithm based on histogram of oriented gradients technique is proposed. The Extended Kalman Filter (EKF) is used to estimate the 3-DoF motion of the robot along with two-dimensional positions of vertical lines in the environment. In order to overcome bearing-only initialization, the Unscented Transform is used to estimate the probability distribution function (PDF) of an initialized vertical line. Simulations and real experiments have been carried out to validate the proposed algorithm.
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