Histograms-Based Visual Servoing

In the direct visual servoing methods such as photometric framework, the images as a whole are used to define a control law. This can be opposed to the classical visual servoing approaches that relies on geometric features and where image processing algorithms that extract and track visual features are necessary. In this letter, we propose a generic framework to consider histogram as a visual feature. A histogram is an estimate of the probability distribution of a variable (for example the probability of occurrence in an intensity, color, or gradient orientation in an image). We show that the framework we proposed applies, but is not limited to, a wide set of histograms and allows the definition of efficient control laws. Statistical comparisons are presented from simulation results and real robots experiments including navigation tasks are also provided.