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GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

2019-02-28 12:23:53
Jiexiong Tang, Ludvig Ericson, John Folkesson, Patric Jensfelt

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-SLAM. 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-SLAM using GCNv2 features runs on a Jetson TX2, an embdded low-power platform. Experimental results show that GCNv2 retains almost the same accuracy as GCN and that it is robust enough to use for control of a flying drone.

Abstract (translated)

URL

https://arxiv.org/abs/1902.11046

PDF

https://arxiv.org/pdf/1902.11046.pdf


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