Using Brightness and Saturation to Visualize Belief and Uncertainty

In developing a visual language for mobile robots, it is necessary to represent the uncertainty present in robot position, obstacle position, and even obstacle presence. In developing a visualization of the robot’s modelof its environment, this uncertainty should be presented to the experimenter, in order to be able to evaluate the extent to which the robot’s sensors and sensor fusion rules are providing consistent and reliable information.In Isaac, a project developing a rule-based visual language for mobile robots, a time-varying diagram is used to represent the robot’s current world model. Hue is used to represent object classes, and brightness is used to represent the degree of belief of an object’s presence. A region in which there is confidence that no object is present is shown as white, while a region with high confidence in the presence of an object is represented with color. Saturation is used to represent confidence in the assessment of object presence (or absence): a totally unsaturated (i.e. grey) area represents an area in which there is no belief at all either in favor of or against the presence of any object; a fully saturated area represents an area in which there is high confidence in the region’s classification. The combination of hue to distinguish between object classes with brightness and saturation for belief and confidence results in a three-dimensional color space for model visualization.Sensor characteristics are encoded in belief functions; upon receiving sensor information, both belief functions and confidence levels can be modified. Belief functions in the presence and absence of obstacles in the modelare maintained through Dempster-Shafer evidentialreasoning.