Graph-based visual odometry for VSLAM

Abstract

Purpose

Typical feature-matching algorithms use only unary constraints on appearances to build correspondences where little structure information is used. Ignoring structure information makes them sensitive to various environmental perturbations. The purpose of this paper is to propose a novel graph-based method that aims to improve matching accuracy by fully exploiting the structure information.

Design/methodology/approach

Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner.

Findings

The authors compare it with several state-of-the-art visual simultaneous localization and mapping algorithms on three datasets. Experimental results reveal that the ORB-G algorithm provides more accurate and robust trajectories in general.

Originality/value

Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner.

Citation

Xu, S., Wang, T., Lang, C., Feng, S. and Jin, Y. (2019), "Graph-based visual odometry for VSLAM", Industrial Robot, Vol. 45 No. 5, pp. 679-687. https://doi.org/10.1108/IR-04-2018-0061