Semi-automated Signal Surveying Using Smartphones and Floorplans

TL;DR

This paper presents PFSurvey, a semi-automated method for creating accurate signal maps using smartphones and floorplans, significantly reducing survey time and improving robustness through novel magnetic loop closure detection.

Contribution

The paper introduces PFSurvey, a novel technique combining sensor data, magnetic field analysis, and floorplan constraints for fast, robust signal map creation.

Findings

Achieves 1.1 m trajectory accuracy 90% of the time

Reduces survey time from days to minutes

Produces signal maps with comparable or better positioning performance

Abstract

Location fingerprinting locates devices based on pattern matching signal observations to a pre-defined signal map. This paper introduces a technique to enable fast signal map creation given a dedicated surveyor with a smartphone and floorplan. Our technique (PFSurvey) uses accelerometer, gyroscope and magnetometer data to estimate the surveyor's trajectory post-hoc using Simultaneous Localisation and Mapping and particle filtering to incorporate a building floorplan. We demonstrate conventional methods can fail to recover the survey path robustly and determine the room unambiguously. To counter this we use a novel loop closure detection method based on magnetic field signals and propose to incorporate the magnetic loop closures and straight-line constraints into the filtering process to ensure robust trajectory recovery. We show this allows room ambiguities to be resolved. An entire building can be surveyed by the proposed system in minutes rather than days. We evaluate in a large office space and compare to state-of-the-art approaches. We achieve trajectories within 1.1 m of the ground truth 90% of the time. Output signal maps well approximate those built from conventional, laborious manual survey. We also demonstrate that the signal maps built by PFSurvey provide similar or even better positioning performance than the manual signal maps.