Title:
SLAM using Visual Scan-Matching with Distinguishable 3D Points
SLAM using Visual Scan-Matching with Distinguishable 3D Points
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Author(s)
Bertolli, Federico
Jensfelt, Patric
Christensen, Henrik I.
Jensfelt, Patric
Christensen, Henrik I.
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Abstract
Scan-matching based on data from a laser scanner is
frequently used for mapping and localization. This paper presents
an scan-matching approach based instead on visual information
from a stereo system. The Scale Invariant Feature Transform
(SIFT) is used together with epipolar constraints to get high
matching precision between the stereo images. Calculating the
3D position of the corresponding points in the world results in
a visual scan where each point has a descriptor attached to it.
These descriptors can be used when matching scans acquired
from different positions.
Just like in the work with laser based scan matching a map
can be defined as a set of reference scans and their corresponding
acquisition point. In essence this reduces each visual scan that
can consist of hundreds of points to a single entity for which only
the corresponding robot pose has to be estimated in the map.
This reduces the overall complexity of the map.
The SIFT descriptor attached to each of the points in the
reference allows for robust matching and detection of loop closing
situations. The paper presents real-world experimental results
from an indoor office environment.
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Date Issued
2006-10
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Article