GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

TL;DR

GCNv2 is a deep learning-based keypoint and descriptor generator designed for real-time SLAM, offering improved efficiency and compatibility with embedded platforms while maintaining accuracy and robustness.

Contribution

The paper introduces GCNv2, a more efficient and embedded-platform-compatible version of GCN for keypoint detection and description in SLAM systems.

Findings

GCNv2 achieves real-time performance on embedded hardware.

GCNv2 maintains accuracy comparable to GCN.

GCNv2 is robust for drone control applications.

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.