The KITTI Vision Benchmark Suite

Method

Trifocal Geometry Visual Odometry [st] [TGVO]

Submitted on 31 Oct. 2012 12:33 by
Bernd Kitt (Karlsruhe Institute of Technology (KIT)

Running time:		0.06 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

Method uses the trifocal geometry between two consecutive
stereo
image pairs combined with an Iterated Sigma Point Kalman Filter
to
estimate the egomotion. Robustness against moving objects and
mismatches is achieved using a RANSAC approach in the
innovation
step of the Kalman Filter.

Runtime does not include the time for feature detection and
matching but only the time needed to estimate the motion of the
camera.

Parameters:

\numSamples = 100
\outlierThreshold = 1.0
\iterationThreshold = 0.001
\stdObservations = 0.5
\usedCameras = LEFT

Latex Bibtex:

@inproceedings{KittEtAl2010,
Author = {Bernd Kitt and Andreas Geiger and Henning
Lategahn},
Booktitle = {IV},
Title = {Visual Odometry based on Stereo Image Sequences
with RANSAC-based Outlier Rejection Scheme},
Year = {2010}}

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.