Robot

Algorithm

Linguistics

Philosophy

Biology

Simultaneous localization and mapping

Scale-invariant feature transform

Drone

Set (abstract data type)

Computer science

Code (set theory)

Genetics

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Feature (linguistics)

Mobile robot

Artificial intelligence

Orb (optics)

Programming language

Feature extraction

Binary number

Computer vision

Arithmetic

IEEE Robotics and Automation Letters

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.

GCNv2: Efficient Correspondence Prediction for Real-Time SLAM