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About

Usama Saqib is pioneering the use of acoustic echoes for robotic spatial awareness, addressing a critical gap where traditional vision and laser-based sensors fail—particularly with transparent surfaces like glass. His core research lies in acoustic signal processing, robot audition, and spatial mapping, where he develops sophisticated estimation frameworks that allow robots to "see" their environment through sound. His most influential work, "A framework for spatial map generation using acoustic echoes for robotic platforms" (17 citations), introduces a non-linear least squares estimator combined with beamforming to construct indoor maps from echo patterns. Saqib has also advanced the theoretical foundations of echo estimation, notably through his application of expectation-maximization (EM) methods for joint time-of-arrival and direction-of-arrival estimation, as demonstrated in his 2019 and 2020 papers (11 and 14 citations respectively). A particularly innovative contribution is his use of a robot's own ego-noise—the sound of its moving parts—to detect nearby reflectors, eliminating the need for external sound sources. With a growing citation impact and a clear trajectory toward enabling autonomous navigation in challenging environments, Saqib's work is establishing acoustic echolocation as a viable sensory modality for next-generation robotics.