Identifying maximal rigid components in bearing-based localization

We present an approach for sensor network localization when provided with a set of angular constraints. This problem arises in camera networks when angles between nearby points can be measured but depth measurements are not readily available. We provide contributions for two different variations on this problem. First, when each node is aware of a global coordinate frame, we present a novel method for identifying the components of the problem that are rigidly constrained. Second, in the more difficult case where only relative angles are known, we propose a novel spectral solution that achieves a globally-optimal embedding under transitively-triangular constraints, which we show encompass a wide range of real-world conditions. We demonstrate the utility of our algorithm on both synthetic data and data from quadrotor robot formations.