Sensor Fusion - Sonar and Stereo Vision, Using Occupancy Grids and SIFT

The main contribution of this paper is to present a sensor fusion approach to scene environment mapping as part of a SDF (Sensor Data Fusion) architecture. This approach involves combined sonar and stereo vision readings. Sonar readings are interpreted using probability density functions to the occupied and empty regions. SIFT (Scale Invariant Feature Transform) feature descriptors are  interpreted using gaussian probabilistic error models. The use of occupancy grids is proposed for representing the sonar  as well as the features descriptors readings. The Bayesian estimation approach is applied to update the sonar and the SIFT descriptors' uncertainty grids. The sensor fusion yields a significant reduction in the uncertainty of the occupancy grid compared to the individual sensor readings.