In the process of determining positioning point by constructing geometric relations on the basis of the positions and poses obtained from multiple pairs of epipolar geometry, the direction vectors will not converge due to the existence of mixed errors. The existing methods to calculate the coordinates of undetermined points directly map the three-dimensional direction vector to the two-dimensional plane and take the intersection points that may be at infinity as the positioning result. To end this, an indoor visual positioning method with three-dimensional coordinates using built-in smartphone sensors based on epipolar geometry is proposed, which transforms the positioning problem into solving the distance from one point to multiple lines in space. It combines the location information obtained by the accelerometer and magnetometer with visual computing to obtain more accurate coordinates. Experimental results show that this positioning method is not limited to a single feature extraction method when the source range of image retrieval results is poor. It can also achieve relatively stable localization results in different poses. Furthermore, 90% of the positioning errors are lower than 0.58 m, and the average positioning error is less than 0.3 m, meeting the accuracy requirements for user localization in practical applications at a low cost.
Keywords: built-in sensors; coordinate transformation; epipolar geometry; indoor localization; pose estimation; visual positioning.