The KITTI Vision Benchmark Suite

Method

[st] Stereo based Localization considering Uncertainty Propagation [SLUP]

Submitted on 21 Nov. 2016 17:10 by
XIAOZHI QU (MATIS, IGN)

Running time:		0.17 s
Environment:		4 cores @ 3.3 Ghz (C/C++)

Method Description:

Local bundle adjustment (LBA)
Uncertainty propagation.
No loop closure.
Key frame selection

Parameters:

LBA window size: 10
Interest point radius: 20 pixels

Latex Bibtex:

@article{QU2017,
title = "Landmark based localization in urban
environment",
journal = "ISPRS Journal of Photogrammetry and
Remote Sensing",
year = "2017",
issn = "0924-2716",
doi = "https://doi.org/10.1016/j.isprsjprs.2017.09.010",
url =
"http://www.sciencedirect.com/science/article/pii/S092
4271617302228",
author = "Xiaozhi Qu and Bahman Soheilian and Nicolas
Paparoditis",
keywords = "Visual odometry",
keywords = "Landmark",
keywords = "Multi-camera",
keywords = "Local bundle adjustment",
keywords = "Uncertainty"
}

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.