Low-bandwidth (Sparse) Visual Simultaneous Localization and Mapping (Sparse-VSLAM)

Research Overview

  • We study the visual SLAM problem under sparse sampling or low-bandwidth setting. Specifically, the sampling rate of the image frames is low (due to e.g., low-bandwith constraint) and the overlap between adjacent frames is small.
  • The main technical challenges include:

    (1) locally, difficulty in obtaining reliable inter-image correlation and matching under small partial overlap,

    (2) globally, large search space to compose global solution from unreliable local alignments, and

    (3) the efficiency requirement for online SLAM computation.


A Multi-frame Graph Matching Algorithm for Low-bandwidth RGB-D SLAM.

S. Zheng, J. Hong, K. Zhang, B. Li, and X. Li

Computer-aided Design (CAD), vol. 78, pp. 107-117, 2016.


Sparse3D: A New Global Model for Matching Sparse RGB-D Dataset with Small Inter-frame Overlap.

C. Le, and X. Li

Computer-aided Design (CAD), 102:33-43, 2018.

[Paper][Bib] [Codes] [Demo]

A Hardware-adaptive Deep Feature Matching Pipeline for Real-time 3D reconstruction.

S. Zheng, Y. Wang, B. Li, and X. Li

Computer-aided Design (CAD), Vol. 132, Article 102984, 2021.

[Paper] [Bib]

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