Indoor positioning and optimizing method based on MEMS sensor and sparse landmark points

Abstract

The invention discloses an indoor positioning and optimization method based on an MEMS sensor and sparse landmark points, and the method comprises the steps: calculating the first position, first speed and attitude information of a target object based on an inertial navigation mechanical arrangement algorithm; obtaining a plurality of pieces of constraint information of a target object, and obtaining an observed quantity and a state quantity of the target object according to the plurality of pieces of constraint information; calculating a second position and a first distance between the preset position and the second position based on the preset position of the sparse landmark point; based on an adaptive unscented Kalman filtering algorithm, fusing the constraint information, the observed quantity, the first position, the first speed, the attitude information, the second position and the first distance to obtain a fused observed quantity, a fused state quantity, a fused position and fused attitude information of the target object; and performing error optimization calculation on the fusion position and the fusion attitude information based on a preset filtering algorithm to obtain a target position. 本发明公开了基于MEMS传感器和稀疏地标点的室内定位及优化方法，所述方法包括：基于惯性导航机械编排算法，计算目标物体的第一位置、第一速度和姿态信息；获取目标物体的若干约束信息，并根据若干所述约束信息得到目标物体的观测量和状态量；基于稀疏地标点的预设位置，计算第二位置以及预设位置与第二位置之间的第一距离；基于自适应无迹卡尔曼滤波算法，将若干所述约束信息、所述观测量、所述第一位置、所述第一速度、所述姿态信息、第二位置和所述第一距离进行融合，得到目标物体的融合观测量、融合状态量、融合位置和融合姿态信息；并基于预设的滤波算法，对所述融合位置和所述融合姿态信息进行误差优化计算，得到目标位置。