The KITTI Vision Benchmark Suite

Method

VISO2-M + MLMSFM GP Estimation [VISO2-M + GP]

Submitted on 24 Apr. 2014 23:43 by
Shiyu Song (UC San Diego)

Running time:		0.15 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

MLMSFM's ground plane estimation algorithm is
integrated into VISO2-M replacing the original one.

Parameters:

Default parameters, except:
height = 1.7
pitch = -0.03
nms_n = 3
half_resolution = 0

Latex Bibtex:

@INPROCEEDINGS{Geiger11,
author = {Andreas Geiger and Julius Ziegler and
Christoph Stiller},
title = {StereoScan: Dense 3d Reconstruction in
Real-time},
booktitle = {IV},
year = {2011},
month = {June},
address = {Baden-Baden, Germany}
}
@inproceedings{song14_visual_odometry,
author = {Shiyu {Song} and Manmohan
{Chandraker}},
title = {Robust Scale Estimation in Real-Time
Monocular SFM for Autonomous Driving},
booktitle = {CVPR},
month = June # { 24-27, },
year = 2014,
address = {Columbus, Ohio, USA}
}

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.