Bayesian generalized kernel inference for occupancy map prediction

Kevin Doherty, Jinkun Wang, Brendan Englot

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

28 Scopus citations

Abstract

We consider the problem of building accurate and descriptive 3D occupancy maps of an environment from sparse and noisy range sensor data. We seek to accomplish this task by constructing a predictive model online and inferring the occupancy probability of regions we have not directly observed. We propose a novel algorithm leveraging recent advances in data structures for mapping, sparse kernels, and Bayesian nonparametric inference. The resulting inference model has several desirable properties in comparison to existing methods, including speed of computation, the ability to be recursively updated without approximation, and consistency between batch and online inference. The method also reverts to the use of a specified prior state when insufficient relevant training data exist to predict the occupancy probability of a query point, a property which is attractive for motion planning and exploration applications with mobile robots.

Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
Pages3118-3124
Number of pages7
ISBN (Electronic)9781509046331
DOIs
StatePublished - 21 Jul 2017
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: 29 May 20173 Jun 2017

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Country/TerritorySingapore
CitySingapore
Period29/05/173/06/17

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