The KITTI Vision Benchmark Suite

A framework that integrates runtime-efficient strategies with robust techniques at various core stages in visual odometry is proposed. Firstly, a pruning method is employed to reduce the computational complexity of KLT feature detection without compromising on the quality of the features. Next, three strategies, i.e. smooth motion constraint, adaptive integration window technique and automatic tracking failure detection scheme, are introduced into the conventional KLT tracker to facilitate generation of feature correspondences in a robust and runtime efficient way. Finally, an early RANSAC termination condition is integrated with the Gauss-Newton optimization scheme to enable rapid convergence of the motion estimation process while achieving robustness.

Up to 500 KLT point features

@article{meiqing2017vo,
title={A Framework for Fast and Robust Visual Odometry},
author={Meiqing, Wu and Siew-Kei, Lam and Thambipillai, Srikanthan},
journal={IEEE Transaction on Intelligent Transportation Systems},
year={2017},
publisher={IEEE}
}