21st International Conference on Advanced Information Networking and Applications (AINA 2007), May 21-23 2007, Niagara Falls Canada

This research was conducted at the Fordham University Robotics and Computer Vision Lab. For more information about graduate programs in Computer Science, see, and the Fordham University Graduate School of Arts and Sciences, see


Computer Engineering | Robotics


We address the problem of sensor fusion for stereo and ultrasound depth measurements for map building for a robot operating in a cluttered environment. In such a situation it’s difficult to make useful and realistic assumpt ions about the sensor or environment statistics. Combinatorial Fusion Analysis is used to develop an approach to fusion with unknown sensor and environment statistics. A metric is proposed that shows when fusion from a set of fusion alternatives will produ ce a more accurate estimation of depth than either sonar or stereo alone and when not. The metric consists of two crit eria: (a) the performance ratio PR(A,B) between sensors A and B, and (b) the diversity d(A,B) between A and B as captured by the rank -score function fA and fB. Experimental results are reported to illustrate that these two CFA criteria are viable predictors to distinguish between positive cas es (the combined system performs better than or equal to the individual systems) and negative cases .

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