Abstract
Visual odometry (VO) is the process of estimating the gomotion of an agent using the input of a single or multiple cameras attached to it. The advantage of VO with respect to wheel odometry is that VO is not affected by wheel slip in uneven terrain or other adverse conditions. During the feature-detection step, the image is searched for salient keypoints that are likely to match well in other images. A local feature is an image pattern that differs from its immediate neighborhood in terms of intensity, color, and texture. In the feature description step, the region around each detected feature is converted into a compact descriptor that can be matched against other descriptors. After comparing all feature descriptors between two images, the best correspondence of a feature in the second image is chosen as that with the closest descriptor. Alternatively, if only the motion model is known but not the 3-D feature position, the corresponding match can be searched along the epipolar line in the second image.
| Original language | English |
|---|---|
| Article number | 6153423 |
| Pages (from-to) | 78-90 |
| Number of pages | 13 |
| Journal | IEEE Robotics & Automation Magazine |
| Volume | 19 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2012 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Computer Science Applications
Cite this
Visual odometry : Part II: Matching, robustness, optimization, and applications. / Fraundorfer, Friedrich; Scaramuzza, Davide.
In: IEEE Robotics & Automation Magazine , Vol. 19, No. 2, 6153423, 2012, p. 78-90.Research output: Contribution to journal › Article › Research › peer-review
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TY - JOUR
T1 - Visual odometry
T2 - Part II: Matching, robustness, optimization, and applications
AU - Fraundorfer, Friedrich
AU - Scaramuzza, Davide
PY - 2012
Y1 - 2012
N2 - Visual odometry (VO) is the process of estimating the gomotion of an agent using the input of a single or multiple cameras attached to it. The advantage of VO with respect to wheel odometry is that VO is not affected by wheel slip in uneven terrain or other adverse conditions. During the feature-detection step, the image is searched for salient keypoints that are likely to match well in other images. A local feature is an image pattern that differs from its immediate neighborhood in terms of intensity, color, and texture. In the feature description step, the region around each detected feature is converted into a compact descriptor that can be matched against other descriptors. After comparing all feature descriptors between two images, the best correspondence of a feature in the second image is chosen as that with the closest descriptor. Alternatively, if only the motion model is known but not the 3-D feature position, the corresponding match can be searched along the epipolar line in the second image.
AB - Visual odometry (VO) is the process of estimating the gomotion of an agent using the input of a single or multiple cameras attached to it. The advantage of VO with respect to wheel odometry is that VO is not affected by wheel slip in uneven terrain or other adverse conditions. During the feature-detection step, the image is searched for salient keypoints that are likely to match well in other images. A local feature is an image pattern that differs from its immediate neighborhood in terms of intensity, color, and texture. In the feature description step, the region around each detected feature is converted into a compact descriptor that can be matched against other descriptors. After comparing all feature descriptors between two images, the best correspondence of a feature in the second image is chosen as that with the closest descriptor. Alternatively, if only the motion model is known but not the 3-D feature position, the corresponding match can be searched along the epipolar line in the second image.
UR - http://www.scopus.com/inward/record.url?scp=84862585064&partnerID=8YFLogxK
U2 - 10.1109/MRA.2012.2182810
DO - 10.1109/MRA.2012.2182810
M3 - Article
VL - 19
SP - 78
EP - 90
JO - IEEE Robotics & Automation Magazine
JF - IEEE Robotics & Automation Magazine
SN - 1070-9932
IS - 2
M1 - 6153423
ER -