Combinatorial Fusion Criteria for Robot Mapping

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 assumptions 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 produce a more accurate estimation of depth than either sonar or stereo alone and when not. The metric consists of two criteria: (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 cases (the combined system performs better than or equal to the individual systems) and negative cases.