Circular Regression Based on Gaussian Processes

Circular data is very relevant in many fields such as Geostatistics, Mobile Robotics and Pose Estimation. However, some existing angular regression methods do not cope with arbitrary nonlinear functions properly. Moreover, some other regression methods that do cope with nonlinear functions, like Gaussian Processes, are not designed to work well with angular responses. This paper presents two novel methods for circular regression based on Gaussian Processes. The proposed methods were tested on both synthetic data from basic functions, and real data obtained from a computer vision application. In these experiments, both proposed methods showed superior performance to that of Gaussian Processes.