The KITTI Vision Benchmark Suite

Method

PbT-M1 [PbT-M1]

Submitted on 18 Apr. 2017 14:43 by
Nolang Fanani (Goethe University of Frankfurt)

Running time:		1 s
Environment:		1 core @ 2.5 Ghz (Python + C/C++)

Method Description:

Monocular visual odometry using propagation-based tracking

Parameters:

n=1

Latex Bibtex:

@inproceedings{FananiBradlerOchsMester2016,
title={Keypoint trajectory estimation using propagation based tracking},
author={Fanani, Nolang and Ochs, Matthias and Bradler, Henry and Mester, Rudolf},
booktitle={Intelligent Vehicles Symposium (IV)},
pages={933--939},
year={2016},
organization={IEEE}
}
@inproceedings{FananiStuerckBarnadaMester2017,
title={Multimodal scale estimation for monocular visual odometry},
author={Fanani, Nolang and Stuerck, Alina and Barnada, Marc and Mester, Rudolf},
booktitle={Intelligent Vehicles Symposium (IV)},
year={2017},
organization={IEEE}
}

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.