Learning a Spatial Field with Gaussian Process Regression in Minimum Time

Suryan, Varun; Tokekar, Pratap

doi:10.1007/978-3-030-44051-0_18

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Chen and Liu introduced Pareto MCTS, an anytime multi-objective planning method addressing exploration vs. exploitation [13]. While many informative planning studies assume known hyperparameters [2], [14]- [17], online planning estimates them during execution. Binney et al [16] used initial run data for estimation.…”

Section: Related Workmentioning

confidence: 99%

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Learning a Spatial Field in Minimum Time With a Team of Robots

Suryan

Tokekar

2020

IEEE Trans. Robot.

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The mean percentage difference between the empirical MSE and the posterior variance. The mean was calculated over a set of grid locations inside the farm. 4.3 Disk placement covering the farm calculated by DC algorithm. The disks are of radii 3r max which are concentric with disks of radii r max in I. The approximate optimal TSP tour visiting the centers is shown in blue. The lawnmower detours have been omitted to make the figure more legible.. .

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Section: Related Workmentioning

confidence: 99%

“…A preliminary version of this work was presented at the 13 th International Workshop on the Algorithmic Foundations of Robotics (WAFR'18) [17]. In the preliminary version we provided guarantees for the chance constraints of incorrect predictions.…”

Section: Introductionmentioning

confidence: 99%

Learning a Spatial Field in Minimum Time With a Team of Robots

Suryan

Tokekar

2020

IEEE Trans. Robot.

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“…They tested their model with two adaptive sampling strategies. Suryan and Tokekar [44] develop a fast GP regression informative path planning algorithm over spatial fields. Berget et al [45], Fossum and Eidvsik et al [46], and Fossum and Fragoso et al [10] implement GP regression on AUVs and demonstrate the viability of simple IPP algorithms in realworld scenarios.…”

Section: Related Workmentioning

confidence: 99%

Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations

Soucie

Sosik

Girdhar

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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We propose a generative model for the spatiotemporal distribution of high dimensional categorical observations. These are commonly produced by robots equipped with an imaging sensor such as a camera, paired with an image classifier, potentially producing observations over thousands of categories. The proposed approach combines the use of Dirichlet distributions to model sparse co-occurrence relations between the observed categories using a latent variable, and Gaussian processes to model the latent variable's spatiotemporal distribution. Experiments in this paper show that the resulting model is able to efficiently and accurately approximate the temporal distribution of high dimensional categorical measurements such as taxonomic observations of microscopic organisms in the ocean, even in unobserved (held out) locations, far from other samples. This work's primary motivation is to enable deployment of informative path planning techniques over high dimensional categorical fields, which until now have been limited to scalar or low dimensional vector observations.

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Learning a Spatial Field with Gaussian Process Regression in Minimum Time

Cited by 2 publications

References 19 publications

Learning a Spatial Field in Minimum Time With a Team of Robots

Learning a Spatial Field in Minimum Time With a Team of Robots

Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations

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