Blind as a bat: audible echolocation on small robots

For safe and efficient operation, mobile robots need to perceive their environment, and in particular, perform tasks such as obstacle detection, localization, and mapping. Although robots are often equipped with microphones and speakers, the audio modality is rarely used for these tasks. Compared to the localization of sound sources, for which many practical solutions exist, algorithms for active echolocation are less developed and often rely on hardware requirements that are out of reach for small robots. We propose an end-to-end pipeline for sound-based localization and mapping that is targeted at, but not limited to, robots equipped with only simple buzzers and low-end microphones. The method is model-based, runs in real time, and requires no prior calibration or training. We successfully test the algorithm on the e-puck robot with its integrated audio hardware, and on the Crazyflie drone, for which we design a reproducible audio extension deck. We achieve centimeter-level wall localization on both platforms when the robots are static during the measurement process. Even in the more challenging setting of a flying drone, we can successfully localize walls, which we demonstrate in a proof-of-concept multi-wall localization and mapping demo.Comment: 8 pages, 10 figures, published in IEEE Robotics and Automation Letter

GeoAware: A Hybrid Indoor and Outdoor Localization Agent for Smart Buildings

Localizing, identifying and authenticating the individual occupants is of paramount importance for the future intelligent buildings. Although the performance of outdoor positioning systems is sufficiently good, the indoor ones have still to converge to a universal interoperable technology. This paper proposes a hybrid, unified localization architecture for indoor and outdoor tracking of the building occupants. By taking advantage of the smartphones and their recent near field communication (NFC) capabilities; a low cost, accurate and scalable localization solution is proposed. This system is a module of an existing, modern building management system (BMS) to which it offers location-aware, energy and comfort management capabilities. The solution is currently deployed as a medium scale trial; therefore, the self-energy use, reliability, ease of use and the privacy requirements are of paramount importance. The system analysis in this paper additionally includes accuracy and battery impact assessment in real-world use cases and location-aware building management operations