A rapid optimization method for visual indirect SLAM using a subset of feature points

Ryosuke Kazami, Hideharu Amano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a novel optimization method for indirect Simultaneous Localization And Mapping(SLAM). This method avoids error-intended map points using a random subset of extracted feature points. As a result, this method making the calculation time short at the cost of some accuracy. The published code of ORB-SLAM2 has longer calculation time compared to other systems such as Semi-direct odometry(SVO). This is due to feature extraction time of an indirect method, which makes the indirect methods robust. However, considering CPU performance on edge devices like MAVs, a lightweight system is welcome. ORB-SLAM2 is widely known as a robust and rapid visual Simultaneous-Localization-And-Mapping (vSLAM) method than before. It extracts FAST feature points from a given image and optimizes using all of these points for camera-posing estimation. However, some points disturb accurate estimation causing large reprojection error after optimization. Although these points are eliminated as outliers after optimization, they may be an obstacle for accurate pose estimation. To make this system more robust and more rapid, We suggest an improved method to estimate camera-posing, where we use a subset of extracted points for estimation, avoiding mixing large-reprojection-error points in bundle adjustment. The experimental results on the SLAM benchmark dataset KITTI odometry demonstrated that the proposed method outperformed the original implementation of ORB-SLAM2 from the viewpoint of calculation time.

Original languageEnglish
Title of host publicationProceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages275-279
Number of pages5
ISBN (Electronic)9781728152684
DOIs
Publication statusPublished - 2019 Nov
Event7th International Symposium on Computing and Networking Workshops, CANDARW 2019 - Nagasaki, Japan
Duration: 2019 Nov 262019 Nov 29

Publication series

NameProceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019

Conference

Conference7th International Symposium on Computing and Networking Workshops, CANDARW 2019
CountryJapan
CityNagasaki
Period19/11/2619/11/29

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Keywords

  • Bundle adjustment
  • Point cloud
  • SLAM

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems
  • Artificial Intelligence
  • Computer Networks and Communications

Cite this

Kazami, R., & Amano, H. (2019). A rapid optimization method for visual indirect SLAM using a subset of feature points. In Proceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019 (pp. 275-279). [8951546] (Proceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CANDARW.2019.00055