On Sensor Network Localization Exploiting Topological Constraints

We present a novel approach to localize an unknown planar sensor network based on sparse sampling of partially observable paths traversed by moving agents. The problem is inspired by mapping the geometry of a building floorplan via "uncooperative sensing", using data from camera feeds and other tracking-capable sensors. Unique challenges include having no knowledge of sensor placement, coverage or their extrinsic parameters nor the knowledge of the motion of the people within a floorplan. The methods used are, at first, topological, to build a combinatorial model with the appropriate topology. This model is then augmented to include weak geometric information, and optimization techniques are used to approximate the domain. Topological information is captured within the optimization problem to constrain the solution.