The KITTI Vision Benchmark Suite

We present a real-time monocular SFM system that
corrects for scale drift using a novel cue
combination framework for ground plane estimation.
Our ground plane estimation uses multiple cues
like sparse features, dense inter-frame stereo. A
data driven mechanism is proposed to learn models
from training data that relate observation
covariances for each cue to error behavior of its
underlying variables. During testing, this allows
per-frame adaptation of observation covariances
based on relative confidences inferred from visual
data.

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@inproceedings{Song2014CVPR,
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}
}
@inproceedings{song13_visual_odometry,
author = {Shiyu {Song} and Manmohan {Chandraker}
and
Clark C. {Guest}},
title = {Parallel, Real-time Monocular Visual
Odometry},
booktitle = {ICRA},
month = May # { 6-10, },
year = 2013,
address = {Karlsruhe, Germany}
}