Indoor positioning technology based on map information perception
With the increasing demand for location-based services, indoor positioning technology has become one of the most attractive areas of research. Microelectromechanical systems sensors in smart terminals are used to realise a pedestrian dead reckoning algorithm. Owing to the accumulated error increased with time, the results of positioning will produce a large error, and an indoor positioning method based on the perception and constraint of map information is designed including straight path constraint and inflection point constraint. In the method of inflection point constraint, several common machine learning algorithms are compared through the experiments, and the secondary discriminant method is utilised to detect the inflection point with a detection accuracy of 97.62%. Finally, the performances of the improved algorithm and the traditional dead reckoning algorithm are compared in the experiments. The results show that the average positioning accuracy of the improved algorithm is 0.073 m, the positioning accuracy within 1 m reaches 100%, it is obviously higher than that of the traditional positioning algorithm and the effectiveness of the algorithm is verified.