The KITTI Vision Benchmark Suite

Method

Good Feature Matching [GFM]
https://github.com/YipuZhao/GF_ORB_SLAM2

Submitted on 26 Apr. 2019 23:14 by
Yipu Zhao (Georgia Institute of Technology)

Running time:		0.03 s
Environment:		2 cores @ 1.5 Ghz (C/C++)

Method Description:

Good feature matching is an enhancement module
that is designed for feature-based BA SLAM, such
as ORB-SLAM.
The main advantage of good feature matching, as
opposed to the conventional batch feature
matching, is the better trade-off of performance-
efficiency.

Parameters:

160 good features are matched per stereo pair (left
+ right)

Latex Bibtex:

@article{zhao2019good,
title={Good Feature Matching: Towards Accurate,
Robust VO/VSLAM with Low Latency},
author={Zhao, Yipu and Vela, Patricio A.},
journal={submitted to IEEE Transactions on
Robotics},
year={2019}
}

Detailed Results

From all test sequences (sequences 11-21), our benchmark computes translational and rotational errors for all possible subsequences of length (5,10,50,100,150,...,400) meters. Our evaluation ranks methods according to the average of those values, where errors are measured in percent (for translation) and in degrees per meter (for rotation). Details for different trajectory lengths and driving speeds can be found in the plots underneath. Furthermore, the first 5 test trajectories and error plots are shown below.