A critical problem in sensory interpretation is the derivation of the right mapping between sensor signals and properties of the world. In this regard, the method proposed uses active sensing to acquire environmental features for reliable navigation in indoor environments. The features utilized are lines, corners, and edges extracted from common indoor room features such as walls, room corners and room edges, respectively. These features are abundant in indoor environments and allow for closed-form location estimates from time-of-flight sonar information. Subsequently, a multiple hypothesis testing procedure is utilized to accurately localize and identify those features. Experiments using a new active sonar system, implemented using a 3 DOF binaural sonar head, shows that the system is able to correctly localize and identify indoor environmental features, and that active sensing strategies based on sensor reconfiguration makes the process of feature extraction more accurate.

This work explores techniques for sonar sensor fusion in the context of environmental feature detection and identification for navigation tasks. By detecting common features in indoor environments and using them as landmarks, a robot can navigate reliably, recovering its pose when necessary. Preliminary results on a multiple hypothesis testing procedure for feature localization and identification show that accurate feature information can be acquired with adequate sonar models and configurations. In addition, a method that associates sonar configuration with the precision of feature extraction is discussed, as well as its utility for guiding an active sonar sensor.