GCNv2: Efficient Correspondence Prediction for Real-Time SLAM
Jiexiong Tang, Ludvig Ericson, John Folkesson, Patric Jensfelt
Abstract
In this letter, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D projective geometry. GCNv2 is designed with a binary descriptor vector as the ORB feature so that it can easily replace ORB in systems such as ORB-SLAM2. GCNv2 significantly improves the computational efficiency over GCN that was only able to run on desktop hardware. We show how a modified version of ORBSLAM2 using GCNv2 features runs on a Jetson TX2, an embedded low-power platform. Experimental results show that GCNv2 retains comparable accuracy as GCN and that it is robust enough to use for control of a flying drone. Source code is available at: https://github.com/jiexiong2016/GCNv2_SLAM.
BibTeX
@inproceedings{ral2019_gcnv2efficientco,
title = {GCNv2: Efficient Correspondence Prediction for Real-Time SLAM},
author = {Jiexiong Tang and Ludvig Ericson and John Folkesson and Patric Jensfelt},
booktitle = {RA-L 2019},
year = {2019}
}