Smartphone indoor positioning method based on vision and Wi-Fi double-layer feature map

Abstract:

We proposed a smartphone positioning method by formulating the positioning problem as an HMM(hidden markov model,HMM) based on the proposed double-layer feature map consisting of visual and Wi-Fi features(vision-CSI map,V-CSI map) to solve the issue of low accuracy and poor stability in indoor environment. The V-CSI map is modeled by encoding CSI fingerprint features based on grid and visual features of sparse safety exits as well as association locations. The location problem based on the V-CSI feature map is solved as HMM problem in the method. First, the safety exit sign detection and visual feature matching are completed in the visual positioning phase, and the positioning results are employed to initialize and reinitialize the states of HMM. Subsequently, CSI fingerprint features are matched with that of the V-CSI map to complete the emission probability, and the state transition probability is computed by modeling motion constraint with Gaussian model. Finally, the optimal state is derived from the forward algorithm, and the position of the smartphone is readily determined from the weighted average of the closest states. In the experiment, the proposed method is verified in an office building of 6 000 square meters and an underground parking lot of 3 600 square meters respectively. Experimental results show that the average positioning error of the algorithm is about 1.0 m, and the time of a single positioning is about 170 ms in the two typical indoor scenes. Compared with only CSI positioning methods, the average positioning error of our proposed method is reduced by more than 56%. The outstanding performance of experimental results also illustrates that our proposed method can improve the accuracy and robustness of indoor positioning.