Local_INN: Implicit Map Representation and Localization with Invertible Neural Networks

Zirui Zang, Hongrui Zheng, Johannes Betz, Rahul Mangharam

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

1 Scopus citations

Abstract

Robot localization is an inverse problem of finding a robot's pose using a map and sensor measurements. In recent years, Invertible Neural Networks (INN s) have successfully solved ambiguous inverse problems in various fields. This paper proposes a framework that approaches the localization problem with INN. We design a network that provides implicit map representation in the forward path and localization in the inverse path. By sampling the latent space in evaluation, Local_INN outputs robot poses with covariance, which can be used to estimate the uncertainty. We show that the localization performance of Local_INN is on par with current methods with much lower latency. We show detailed 2D and 3D map reconstruction from Local_INN using poses exterior to the training set. We also provide a global localization algorithm using Local_INN to tackle the kidnapping problem.

Original languageEnglish
Title of host publicationProceedings - ICRA 2023
Subtitle of host publicationIEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages11742-11748
Number of pages7
ISBN (Electronic)9798350323658
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2023-May
ISSN (Print)1050-4729

Conference

Conference2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/232/06/23

Fingerprint

Dive into the research topics of 'Local_INN: Implicit Map Representation and Localization with Invertible Neural Networks'. Together they form a unique fingerprint.

Cite this