Pseudo Inputs Optimisation for Efficient Gaussian Process Distance Fields

Robots reason about the environment through dedicated representations. Despite the fact that Gaussian Process (GP)-based representations are appealing due to their probabilistic and continuous nature, the cubic computational complexity is a concern. In this paper, we present a novel efficient GP-based representation that has the ability to produce accurate distance fields and is parameterised by the optimal locations of pseudo inputs. When applying the proposed method together with a kernel approximation approach, we show it outperforms well-established sparse GP frameworks in efficiency and accuracy. Moreover, we extend the proposed method to work in a dynamic setting, where a map is built iteratively and the scene dynamics are accounted for by adding or removing objects from the environment representation. In a nutshell, our method provides the ability to infer dynamic distance fields and achieve state-of-the-art reconstruction efficiently.