A Backtracking Particle Filter for fusing building plans with PDR displacement estimates

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Abstract
It is known that Particle Filter and Map Filtering techniques can be used to improve the performance of positioning systems, such as Pedestrian Dead Reckoning (PDR). In previous research on indoor navigation, it was generally assumed that detailed building plans were available. However, in many emer gency / rescue scenarios, there may be only limited building plan information on hand. The purpose of this paper is to show how a novel Backtracking Particle Filter (BPF) can be combined with different levels of building plan detail to improve PDR performance. We use real PDR stride length and blunder-prone stride azimuth data which were collected from multiple walks along paths in and out of a small office building. The PDR displacement data is input to the BPF estimator that in turn uses the building plan information to constrain particle motions. The BPF can take advantage of long-range (geometrical) constraint information and yields excellent positioning performance (1.32 m mean 2D error) with detailed building plan information. More significantly, this same filter using only external wall information produces dramatically improved positioning performance (1.89 m mean 2D error) relative to a PDR-only, no map base case (8.04 m mean 2D error). This effect may very well occur for many other realistic wall layouts and path geometries. Moreover, this result has a substantial practical significance since this level of building plan detail could be quickly and easily generated in many emergency instances.

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