Incremental Map Modeling for Lightweight SLAM via Deep Reinforcement Learning

Chien Heng Yu, Ching Chun Huang

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

Abstract

Simultaneous localization and mapping (SLAM) technology which builds a map in a pose graph has high accuracy but low memory utilization and limitations in handling full memory. This paper proposes a keyframe decision-making method based on the visual SLAM framework and deep reinforcement learning. The keyframe selection instruction is given to the mapper, and the map size is controlled to achieve map modeling. Furthermore, the map increment obtained by 3D reconstruction can obtain the intersection relationship between keyframes. We use a self-defining reward function to create the network learning policy to maximize map coverage and minimize memory usage. Experiments demonstrate that our method can perform accurate map modeling without affecting the quality of the mapping, thus reducing memory requirements.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Consumer Electronics, ICCE 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665491303
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Consumer Electronics, ICCE 2023 - Las Vegas, United States
Duration: 6 Jan 20238 Jan 2023

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2023-January
ISSN (Print)0747-668X

Conference

Conference2023 IEEE International Conference on Consumer Electronics, ICCE 2023
Country/TerritoryUnited States
CityLas Vegas
Period6/01/238/01/23

Keywords

  • deep reinforcement learning
  • keyframe
  • SLAM

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