Cooperative relative robot localization with audible acoustic sensing
We describe a method for estimating the relative poses of a team of mobile robots using only acoustic sensing. The relative distances and bearing angles of the robots are estimated using the time of arrival of audible sound signals on stereo microphones. The robots emit specially designed sound waveforms that simultaneously enable robot identification and time of arrival estimation. These acoustic observations are then combined with odometry to update a belief state describing the positions and heading angles of all the robots. To efficiently resolve the ambiguity in the heading angle of the observing robot as well as the back-front ambiguity of the observed robot, we employ a Rao-Blackwellised particle filter (RBPF) where the distribution over heading angles is represented by a discrete set of particles, and the uncertainty in the translational positions conditioned on each of these particles is described by a Gaussian. This approach combines the representational accuracy of conventional particle filters with the efficiency of Kalman filter updates in modeling the pose distribution over a number of robots. We demonstrate how the RBPF can quickly resolve uncertainties in the binaural acoustic measurements and yield a globally consistent pose estimate. Simulations as well as an experimental implementation on robots with generic sound hardware illustrate the accuracy and the convergence of the resulting pose estimates.