Describes a sensor that fuses sonar and visual data to create a three dimensional (3D) model of the environment with application to robot navigation. The environment is characterized by a set of connected horizontal and vertical lines. 3D sonar data is augmented by making deductions concerning the connection and definition of lines in 2D visual data. Any errors that may result from incorrect interpretation of the 2D camera data, such as false connections between lines, can be detected by moving the robot. Experimental results from the sensor are presented.

This paper presents a novel sonar sensor consisting of three transmitters and three receivers that can localise and classify 3D targets into 16 different naturally occurring indoor classes. The sensor produces submillimeter range and sub-degree bearing accuracies using an optimal matched filter time of flight estimator up to a range of 6 meters. The sensor configuration, hardware and processing are described. Experimental results from the sensor are presented.