An entropy optimization strategy for simultaneous localization and mapping

We present a novel algorithm for simultaneous localization and mapping via application of entropy on construction of segment-based maps. Entropy has been incorporated in SLAM to enhance its sensitivity and robustness in presence of non-Gaussian uncertainties and disturbances. The kernel density estimator is employed to approximate the probability appearance of samples directly from sensor data. An entropy based robust estimator is then designed to extract reliable parameters of the line segment from the environment. Rao-Blackwellized particle filter is also adopted to estimate the pose of the robot and update the map simultaneously. Simulations and experiments results validate the effectiveness and accuracy of the proposed approach.