Warped Gaussian Processes Occupancy Mapping with Uncertain Inputs
This work addresses incremental improvements in robotic mapping for scenarios with uncertain inputs and nonlinear noise, enhancing accuracy in specific applications.
The paper tackles occupancy mapping under pose uncertainty and perception noise by developing expected kernel and sub-map methods for uncertain inputs and using Warped Gaussian Processes to handle non-Gaussian noise, resulting in improved map quality with WGPs despite increased uncertainty from pose errors.
In this paper, we study extensions to the Gaussian Processes (GPs) continuous occupancy mapping problem. There are two classes of occupancy mapping problems that we particularly investigate. The first problem is related to mapping under pose uncertainty and how to propagate pose estimation uncertainty into the map inference. We develop expected kernel and expected sub-map notions to deal with uncertain inputs. In the second problem, we account for the complication of the robot's perception noise using Warped Gaussian Processes (WGPs). This approach allows for non-Gaussian noise in the observation space and captures the possible nonlinearity in that space better than standard GPs. The developed techniques can be applied separately or concurrently to a standard GP occupancy mapping problem. According to our experimental results, although taking into account pose uncertainty leads, as expected, to more uncertain maps, by modeling the nonlinearities present in the observation space WGPs improve the map quality.