Surface sensing and classification for efficient mobile robot navigation

Mobile robot navigation and localization is frequently aided by, or even dependent upon, a good estimate of the rate of dead-reckoning error accumulation. Sensor data can be used for position estimation, but this often involves overhead in acquiring and processing the data. By sensing and then classifying the surface type, an estimate of the rate of error accumulation for dead-reckoning allows us to estimate accurately how often localization, including sensor data acquisition, must be performed. In the experiments we describe, a boom-mounted microphone is tapped on different floor materials, much as a blind man might tap his cane. The acoustic signature arising from the contact is then used to classify the floor type by comparing a windowed power spectrum of the acoustic signature with one of a family of prototypical signatures generated statistically from the same material. The technique is low-cost, involves limited computational expense, and performs very well.