Local_INN: Implicit Map Representation and Localization with Invertible Neural Networks

Zirui Zang, Hongrui Zheng, Johannes Betz, Rahul Mangharam

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

1 Zitat (Scopus)

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.

OriginalspracheEnglisch
TitelProceedings - ICRA 2023
UntertitelIEEE International Conference on Robotics and Automation
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten11742-11748
Seitenumfang7
ISBN (elektronisch)9798350323658
DOIs
PublikationsstatusVeröffentlicht - 2023
Extern publiziertJa
Veranstaltung2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, Großbritannien/Vereinigtes Königreich
Dauer: 29 Mai 20232 Juni 2023

Publikationsreihe

NameProceedings - IEEE International Conference on Robotics and Automation
Band2023-May
ISSN (Print)1050-4729

Konferenz

Konferenz2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Land/GebietGroßbritannien/Vereinigtes Königreich
OrtLondon
Zeitraum29/05/232/06/23

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