EchoSLAM: Simultaneous Localization and Mapping with Acoustic Echoes
We address the problem of jointly localizing a robot in an unknown room and estimating the room geometry from echoes. Unlike earlier work using echoes, we assume a completely autonomous setup with (near) collocated microphone and the acoustic source. We first introduce a simple, easy to analyze estimator, and prove that the sequence of room and trajectory estimates converges to the true values. Next, we approach the problem from a Bayesian point of view, and propose a more general solution which does not require any assumptions on motion and measurement model of the robot. In addition to theoretical analysis, we validate both estimators numerically.