Pseudo Inputs Optimisation for Efficient Gaussian Process Distance Fields
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, 00, pp. 7249-7255
- Issue Date:
- 2023-12-13
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Pseudo_Inputs_Optimisation_for_Efficient_Gaussian_Process_Distance_Fields.pdf | Accepted version | 4.36 MB |
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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
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