Efficient active SLAM based on submap joining

Yongbo Chen*, Shoudong Huang, Robert Fitch, Jianqiao Yu

*Corresponding author for this work

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

Abstract

This paper considers the active SLAM problem where a robot is required to cover a given area while at the same time performing simultaneous localization and mapping (SLAM) for understanding the environment and localizing the robot itself. We propose a model predictive control (MPC) framework, and the minimization of uncertainty in SLAM and coverage problems are solved respectively by the Sequential Quadratic Programming (SQP) method. Then, a decision making process is used to control the switching of two control inputs. In order to reduce the estimation and planning time, we use Linear SLAM, which is a submap joining approach. Simulation results are presented to validate the effectiveness of the proposed active SLAM strategy.

Original languageEnglish
Title of host publicationAustralasian Conference on Robotics and Automation, ACRA 2017
EditorsAlen Alempijevic, Teresa Vidal Calleja, Sarath Kodagoda
PublisherAustralasian Robotics and Automation Association
Pages141-147
Number of pages7
ISBN (Electronic)9781510860117
Publication statusPublished - 2017
Externally publishedYes
EventAustralasian Conference on Robotics and Automation, ACRA 2017 - Sydney, Australia
Duration: 11 Dec 201713 Dec 2017

Publication series

NameAustralasian Conference on Robotics and Automation, ACRA
Volume2017-December
ISSN (Print)1448-2053

Conference

ConferenceAustralasian Conference on Robotics and Automation, ACRA 2017
Country/TerritoryAustralia
CitySydney
Period11/12/1713/12/17

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