Network localization from relative bearing measurements

We present an approach for 2D sensor network localization when only bearing measurements are available and no global coordinate frame is known. Our work builds off of the linear constraint given in Kennedy et al. (2012) for sets of nodes that form triangles. We extend that constraint to general networks and present methods for locally optimizing the resulting cost function. We also show how these methods can be used for 3D network localization when the vertical axis is known. The algorithms are evaluated on both synthetic and real datasets, and we also show how they can be applied to the “structure from motion” problem in the field of computer vision.