Cumulative Error Calibrating with Few Landmarks by Matching Human Activity for PDR Indoor Positioning

Severe cumulative errors significantly limit the applicability and expansion of IMU-based indoor localization. A quantitative analysis is conducted showing the impact that heading estimation and step length estimation have on cumulative error. In response, this paper proposes a method that utilizes a few numbers of indoor landmarks to assist IMU localization. Specifically, a lightweight self-attention model is employed to classify behavioral sequences from training data, matching behaviors with landmarks to reconstruct indoor paths. By sequentially linking space-discrete landmarks through timecontinuous behaviors, a spatially reconstructed path is formed within the building, assisting PDR in correcting heading directions based on the resemblance between newly predicted and existing paths. When an activity matches a landmark, the positioning estimate is recalibrated to align with the identified landmark, thereby rectifying cumulative errors. While doing heading estimation, a deep learning technique is applied to mitigate sensor yaw misalignment in the IMU data. The proposed indoor positioning method demonstrates exceptional performance.