Estimation problems like room geometry estimation and localization of acoustic reflectors are of great interest andimportance in robot and drone audition. Several methods for tackling these problems exist, but most of them rely oninformation about times-of-arrival (TOAs) of the acoustic echoes. These need to be estimated in practice, which is adifficult problem in itself, especially in robot applications which are characterized by high ego-noise. Moreover, even ifTOAs are successfully extracted, the difficult problem of echolabeling needs to be solved. In this paper, we proposemultiple expectation-maximization (EM) methods, for jointly estimating the TOAs and directions-of-arrival (DOA) of theechoes, with a uniform circular array (UCA) and a loudspeaker in its center for probing the environment. The differentmethods are derived to be optimal under different noise conditions. The experimental results show that the proposedmethods outperform existing methods in terms of estimation accuracy in noisy conditions. For example, it can provideaccurate estimates at SNR of 10 dB lower compared to TOA extraction from room impulse responses, which is oftenused. Furthermore, the results confirm that the proposed methods can account for scenarios with colored noise orfaulty microphones. Finally, we show the applicability of the proposed methods in mapping of an indoor environment.
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