Echoes generated by the sound reflected off the walls of a room carry information about the geometry of the enclosure. Capitalization of this acoustic property could lead to improvements in current state-of-the-art methods for sound field estimation, where prior information can be used to improve the conditioning of the problem. In this thesis, robust and computational efficient methods are developed for identifying first order reflections to estimate the room geometry using small microphone arrays. Furthermore, as the estimation of such reflections becomes even more challenging in actual audio reproduction systems, this work aims to develop methods capable to deal with complications that might arise due to the employed drivers. This is done by considering the estimation problem in two different scenarios. Firstly, the first order reflections estimation problem is posed as a sorting problem. For this case, a set of echoes, received at different microphones, must be grouped accordingly to the wall which originated them. This problem is solved by using a greedy subspace-based algorithm. The proposed approach provides similar performance compared with the state-of-the-art method at a reduced computational cost. For the second scenario, instead of echoes, only raw microphones measurements are available. This instance of the problem is posed under an estimation theory framework, and solved by sequential minimization of a non-linear cost function based on the propagation of waves. Experimental results, evaluated in simulated shoe-box shaped rooms, demonstrate the performance and applicability of the proposed methods for room geometry estimation.