Smartphone-Based Robot Indoor Localization Using Inertial Sensors, Encoder and Map Matching

In recent years, with the rapid development of robotics, indoor mobile robots have gradually penetrated many aspects of human production and life. High accuracy localization is important for mobile robots in a variety of scenarios, including domestic and public places. In this paper, we present a fusion approach for indoor localization of a smartphone-based robot, which fuses the information of inertial sensors, encoder and indoor maps. In our approach, we first propose a dead reckoning model to obtain the position and attitude of the robot. Secondly, the information obtained by the encoder is used to identify the turning area. The difference between the turning position by our proposed algorithm and the known turning position is compared with a preset threshold. The estimated position converge on its true position. Finally, we design an Extended Kalman Filter (EKF)based fusion algorithm to fuse the inertial data, encoder data and map information. We evaluate our proposed method in an office building. The results show that the mean positioning error of the proposed method on the two routes is 0.7220m and 0.7150m respectively, which is a 60.4% and 68.3% reduction compared with the fusion of DR/Encoder approach.