An Enhanced Visual SLAM Supported by the Integration of plane Features for the Indoor Environment

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Abstract

This paper presents an enhanced indoor RGB-D simultaneously localisation and mapping (SLAM) system based on the integration of plane and point features. A new method was proposed to register each point feature to a corresponding plane feature and then modify its position accordingly. The plane features are parallelly extracted from depth data sources and used jointly to solve the camera pose with point features. Plane features are stored on the map as the same as point features. Both point and plane features are used for backend optimisation, where the weights associated with features can be dynamically updated. At the same time, the on-plane feature points are fixed during the optimisation. The proposed method has been tested with open-source benchmarks, including the scenarios with or without a structured environment. Experiment results demonstrated that the proposed algorithm performs better than other widely cited visual SLAM systems in some structured environments.
Original languageEnglish
Title of host publicationProceedings of the IEEE 12th International Conference Indoor Positioning and Indoor Navigation
PublisherIEEE
Number of pages8
ISBN (Electronic)978-1-7281-6218-8
ISBN (Print)978-1-7281-6219-5
Publication statusPublished (in print/issue) - 26 Oct 2022
EventTwelfth International Conference on Indoor Positioning and Indoor Navigation - Beijing, China
Duration: 5 Sep 20227 Sep 2022
Conference number: 12
http://www.ipin-conference.org/2022/index.html

Publication series

Name
ISSN (Print)2162-7347
ISSN (Electronic)2471-917X

Conference

ConferenceTwelfth International Conference on Indoor Positioning and Indoor Navigation
Abbreviated titleIPIN 2022
Country/TerritoryChina
CityBeijing
Period5/09/227/09/22
Internet address

Keywords

  • SLAM
  • Visual SLAM
  • Geometry Information
  • Plane Feature

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