GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

February 28, 2019 · Entered Twilight · 🏛 IEEE Robotics and Automation Letters

"Last commit was 6.0 years ago (≥5 year threshold)"

Evidence collected by the PWNC Scanner

Repo contents: .gitignore, CMakeLists.txt, Dependencies.md, GCN2, LICENSE.txt, License-gpl.txt, README.md, Thirdparty, Vocabulary, build.sh, cmake_modules, gcn.gif, include, orb.gif, src

Authors Jiexiong Tang, Ludvig Ericson, John Folkesson, Patric Jensfelt arXiv ID 1902.11046 Category cs.RO: Robotics Cross-listed cs.CV Citations 175 Venue IEEE Robotics and Automation Letters Repository https://github.com/jiexiong2016/GCNv2_SLAM ⭐ 859 Last Checked 1 month ago

Abstract

In this paper, 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 ORB-SLAM2 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.