Towards In-Air Ultrasonic QR Codes: Deep Learning for Classification of Passive Reflector Constellations
This work addresses the need for reliable autonomous systems in environments where visual sensors fail, though it is incremental as it builds on prior classification of individual acoustic landmarks.
The paper tackled the problem of increasing information capacity for acoustic landmark systems by introducing reflector constellations as encoded tags, and the result was a multi-label CNN that validated the feasibility of decoding these patterns on a small dataset.
In environments where visual sensors falter, in-air sonar provides a reliable alternative for autonomous systems. While previous research has successfully classified individual acoustic landmarks, this paper takes a step towards increasing information capacity by introducing reflector constellations as encoded tags. Our primary contribution is a multi-label Convolutional Neural Network (CNN) designed to simultaneously identify multiple, closely spaced reflectors from a single in-air 3D sonar measurement. Our initial findings on a small dataset confirm the feasibility of this approach, validating the ability to decode these complex acoustic patterns. Secondly, we investigated using adaptive beamforming with null-steering to isolate individual reflectors for single-label classification. Finally, we discuss the experimental results and limitations, offering key insights and future directions for developing acoustic landmark systems with significantly increased information entropy and their accurate and robust detection and classification.