Long-Term Autonomous Ocean Monitoring with Streaming Samples

Autor:	Chen, Weizhe, Liu, Lantao
Rok vydání:	2023
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method - standard Gaussian process (GP) regression - becomes inadequate in processing the growing sensing data of a large size. To overcome the computational challenge, this paper presents an environmental modeling framework using a sparse variant of GP called streaming sparse GP (SSGP). The SSGP is able to handle streaming data in an online and incremental manner, and is therefore suitable for long-term autonomous environmental monitoring. The SSGP summarizes the collected data using a small set of pseudo data points that best represent the whole dataset, and updates the hyperparameters and pseudo point locations in a streaming fashion, leading to high-quality approximation of the underlying environmental model with significantly reduced computational cost and memory demand. Comment: Proceedings of OCEANS 2019, SEATTLE
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.06578 Zobrazit plný text záznamu View this record from Arxiv