Selection of fusion operations using rank-score diversity for robot mapping and localization

Abstract

In this paper, we evaluate the use of a rank-score diversity measure for selecting sensory fusion operations for a robot localization and mapping application. Our current application involves robot mapping and navigation in an outdoor urban search and rescue situation in which we have many similar and mutually occluding landmarks. The robot is a 4-wheel direct drive platform equipped with visual, stereo depth and ultrasound sensors. In such an application it's difficult to make useful and realistic assumptions about the sensor or environment statistics. Combinatorial Fusion Analysis(CFA) is used to develop an approach to fusion with unknown sensor and environment statistics. A metric is proposed that will indicate 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. Experimental results are reported to illustrate that two CFA criteria are viable predictors to distinguish between positive fusion cases (the combined system performs better than or equal to the individual systems) and negative cases.