Virtual Maps for Autonomous Exploration with Pose SLAM

Jinkun Wang, Tixiao Shan, Brendan Englot

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

6 Scopus citations

Abstract

We consider the problem of autonomous mobile robot exploration in an unknown environment taking into account the robot's mapping rate, map uncertainty, and state estimation uncertainty. This paper presents an exploration framework built upon segment-aided pose SLAM adapted for better active localization. We build on our previous work on expectation maximization (EM) exploration, which explicitly models unknown landmarks as latent variables and predicts their expected uncertainty, to resolve the lack of landmark state in denser instances of SLAM. The proposed system comprises path generation, place recognition forecasting, belief propagation and utility evaluation using a virtual map. We analyze the performance in simulated experiments, showing that our algorithm maintains higher coverage speed in exploration as well as lower mapping and localization error. The real-time applicability is demonstrated on an unmanned ground vehicle.

Original languageEnglish
Title of host publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Pages4899-4906
Number of pages8
ISBN (Electronic)9781728140049
DOIs
StatePublished - Nov 2019
Event2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China
Duration: 3 Nov 20198 Nov 2019

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Country/TerritoryChina
CityMacau
Period3/11/198/11/19

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