Scalable sparsification for efficient decision making under uncertainty in high dimensional state spaces

Khen Elimelech, Vadim Indelman

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

Abstract

In this paper we introduce a novel sparsification method for efficient decision making under uncertainty and belief space planning in high dimensional state spaces. By using a sparse version of the state's information matrix, we are able to improve the high computational cost of examination of all candidate actions. We also present an in-depth analysis for the general case of approximated decision making, and use it in order to set bounds over the induced error in potential revenue. The scalability of the method allows balancing between the degree of sparsification and the tolerance for this error, in order to maximize its benefits. The approach differs from recent methods by focusing on improving the decision making process directly, and not as a byproduct of a sparsification of the state inference. Eventually, we demonstrate the superiority of the approach in a SLAM simulation, where we manage to maintain the accuracy of the solution, while demonstrating a significant improvement in run time.

Original languageEnglish
Title of host publicationIROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
Pages5668-5673
Number of pages6
ISBN (Electronic)9781538626825
DOIs
StatePublished - 13 Dec 2017
Event2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada
Duration: 24 Sep 201728 Sep 2017

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
Volume2017-September

Conference

Conference2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Country/TerritoryCanada
CityVancouver
Period24/09/1728/09/17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Scalable sparsification for efficient decision making under uncertainty in high dimensional state spaces'. Together they form a unique fingerprint.

Cite this