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Monocular Camera Localization in 3D LiDAR Maps
Tim Caselitz, Bastian Steder, Michael Ruhnke, Wolfram Burgard
Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2016
caselitz16iros.pdf
Notes:
Localizing a camera in a given map is essential
for vision-based navigation. In contrast to common methods
for visual localization that use maps acquired with cameras, we
propose a novel approach, which tracks the pose of monocular
camera with respect to a given 3D LiDAR map. We employ
a visual odometry system based on local bundle adjustment
to reconstruct a sparse set of 3D points from image features.
These points are continuously matched against the map to
track the camera pose in an online fashion. Our approach to
visual localization has several advantages. Since it only relies on
matching geometry, it is robust to changes in the photometric
appearance of the environment. Utilizing panoramic LiDAR
maps additionally provides viewpoint invariance. Yet lowcost
and lightweight camera sensors are used for tracking.
We present real-world experiments demonstrating that our
method accurately estimates the 6-DoF camera pose over long
trajectories and under varying conditions.
BibTeX:
@inproceedings{caselitz16iros,
author = {Tim Caselitz and Bastian Steder and Michael Ruhnke and Wolfram Burgard},
title = {Monocular Camera Localization in 3D LiDAR Maps},
booktitle = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = 2016,
url = {http://ais.informatik.uni-freiburg.de/publications/papers/caselitz16iros.pdf}
}
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