The KITTI Vision Benchmark Suite

Method

Stereo Double Window Optimization [st] [Stereo DWO]
https://github.com/JzHuai0108/ORB_SLAM

Submitted on 8 Oct. 2015 19:45 by
Jianzhu Huai (the Ohio State University)

Running time:		0.1 s
Environment:		4 cores @ 2.5 Ghz (C/C++)

Method Description:

Using libviso2, features are matched between the current and previous frame, and the initial inter-frame motion is estimated. Then, points in a local map (organized by a double window) are tracked in the current frame. Finally, observations in the double window are used to refine variables in it by bundle adjustment.

Parameters:

libviso2 inlier threshold sqrt(5.991), refinement option 1; Temporal window size 7, spatial window size 8, horizontal field of view scale factor 0.6; In optimization for each frame, 5 iterations of structure only BA, 5 iterations of joint BA, inlier threshold sqrt(5.991)

Latex Bibtex:

@inproceedings{Huai2015stereo,
title={Stereo-inertial odometry using nonlinear optimization},
author={Huai, Jianzhu and Toth, Charles and Grejner-Brzezinska, Dorota},
booktitle={Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2015)},
year={2015},
organization={ION}
}

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.