Can a Robot Hear the Shape and Dimensions of a Room Knowing the geometry of a space is desirable for many applications, e.g. sound source localization, sound field reproduction or auralization. In circumstances where only acoustic signals can be obtained, estimating the geometry of a room is a challenging proposition. Existing methods have been proposed to reconstruct a room from the room impulse responses (RIRs). However, the sound source and microphones must be deployed in a feasible region of the room for it to work, which is impractical when the room is unknown. This work propose to employ a robot equipped with a sound source and four acoustic sensors, to follow a proposed path planning strategy to moves around the room to collect first image sources for room geometry estimation. The strategy can effectively drives the robot from a random initial location through the room so that the room geometry is guaranteed to be revealed. Effectiveness of the proposed approach is extensively validated in a synthetic environment, where the results obtained are highly promising.

Towards Soundsource Position Estimation using Mutual Information for Next Best View Motion Planning Robotic operations in the real world rely on bearingonly sensors, e.g., optical, acoustic, etc., to sense physical phenomena. Bearingonly sensors are limited because they do not provide range information. To overcome this limitation in estimating source locations, the most common solutions involve making multiple measurements from different locations, either through multiple sensors in the field or a single moving sensor, and then applying triangulation or filtering. In unknown environments with single motioncapable sensors (e.g., mobile robots with onboard sensors), planned motion of the sensor can allow accurate and efficient source position estimation. This paper presents a novel approach in estimating the locations of stationary sources, using a motioncapable sensor. The proposed method combines the concepts of Extended Kalman Filter (EKF) and Mutual Information (MI) from information theory to estimate the Next Best View (NBV) pose to which the sensor should be moved. A utility function, that accounts for the movement cost, the characteristics of the sensor, and the MI and EKF information, facilitates efficient estimation. The proposed algorithm has been implemented in the realworld for Soundsource Position Estimation (SPE), using an acoustic sensor mounted at the end of a 6 degreesoffreedom (DOF) robotic manipulator. The algorithm, termed as NBVSPE, proves its utility and performance through preliminary indoor and outdoor experiments for sound sources in 3D space.