Fast, accurate Gaussian process occupancy maps via test-data octrees and nested Bayesian fusion

Jinkun Wang, Brendan Englot

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

60 Scopus citations

Abstract

We present a novel algorithm to produce descriptive online 3D occupancy maps using Gaussian processes (GPs). GP regression and classification have met with recent success in their application to robot mapping, as GPs are capable of expressing rich correlation among map cells and sensor data. However, the cubic computational complexity has limited its application to large-scale mapping and online use. In this paper we address this issue first by proposing test-data octrees, octrees within blocks of the map that prune away nodes of the same state, condensing the number of test data used in a regression, in addition to allowing fast data retrieval. We also propose a nested Bayesian committee machine which, after new sensor data is partitioned among several GP regressions, fuses the result and updates the map with greatly reduced complexity. Finally, by adjusting the range of influence of the training data and tuning a variance threshold implemented in our method's binary classification step, we are able to control the richness of inference achieved by GPs - and its tradeoff with classification accuracy. The performance of the proposed approach is evaluated with both simulated and real data, demonstrating that the method may serve both as an improved-accuracy classifier, and as a predictive tool to support autonomous navigation.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Robotics and Automation, ICRA 2016
Pages1003-1010
Number of pages8
ISBN (Electronic)9781467380263
DOIs
StatePublished - 8 Jun 2016
Event2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden
Duration: 16 May 201621 May 2016

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2016-June
ISSN (Print)1050-4729

Conference

Conference2016 IEEE International Conference on Robotics and Automation, ICRA 2016
Country/TerritorySweden
CityStockholm
Period16/05/1621/05/16

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