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