Method

Elbrus SLAM [st] [Elbrus]


Submitted on 14 May. 2018 18:17 by
Dmitry Slepichev (NVIDIA)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Elbrus is a stereo Visual Odometry (VO)
technology.

It assumes a calibrated stereo rig
(intrinsics and extrinsics of the cameras are
known, including the baseline distance) and known
lens distortion parameters
(Brown's distortion model).

Elbrus contains two
major components.
The 2D component is based on
multi-pyramid KLT feature tracking with local
average luminance compensation.
The 3D component
uses keyframes selected using 2D track average
motion and rate of disappearance criteria. The 2D
tracking of each feature is guided by the
prediction of the track's position provided by
the results of 3D tracking.

3D tracking is done via the exponential mapping
formalism for
recovering the six absolute orientation
parameters of the camera rig using standard
elements of triangulation, resectioning and
sparse bundle adjustment (SBA).

SBA runs on a
separate thread in parallel with the rest of the
Elbrus VO calculations.
Parameters:
-tracker=klt -lr_tracker=klt -
robustifier_scale=0.05
Latex Bibtex:
@unpublished{ELBRUS2018,
title={Realtime Stereo Visual Odometry},
author={Dmitry Slepichev and Mikhail Smirnov and
Eugene Vendrovsky},
}

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.

Test Set Average


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Sequence 11


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Sequence 12


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Sequence 13


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Sequence 14


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Sequence 15


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