Refinement of Sparsely Tagged Ground Truth Paths Using PDR and Particle Filter Smoothing

Acquiring accurate ground truth or fingerprint data for indoor localization typically demands specialized hardware and considerable effort. This paper introduces three post-processing methods to simplify the recording of such data within certain limitations. The methods can be categorized into fixed-interval smoothing where the complete recording data is available for the optimal state estimation and there is no live processing requirement. Our methods are based on a particle filter system, employing the CONDENSATION algorithm to estimate the hidden state. Particles represent positions and headings in 3D space. They are moved and evaluated using pedestrian dead reckoning based on IMU measurements and a map of the building only. The development and application of these methods is presented in this paper, demonstrating their effectiveness in reducing the effort required for accurate ground truth and fingerprint data recording.