Localization of indoor robot based on particle filter with EKF proposal distribution

The indoor localization of the mobile robot is an important problem. The laser sensor is commonly used in the localization of robot. However, a low-cost LIDAR usually leads to a poor localization result because of the sparse scan points. In face of this problem, map based particle filter localization in indoor environment is realized for the robot equipped with 2D LIDAR. The proposal distribution is obtained from fusing the motion model prior and the most recent measurement by Extended Kalman Filter (EKF). We analyzed the prior distribution and the EKF distribution and realized the particle filter localization on the robot. Experiments show this method is effective.

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