Tim Caselitz, Bastian Steder, Michael Ruhnke, and Wolfram Burgard.
Monocular Camera Localization in 3D LiDAR Maps.
In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2016.

Abstract

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 low-cost 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 entry:

@inproceedings{caselitz16iros,
  author = {Caselitz, Tim and Steder, Bastian and Ruhnke, Michael and Burgard, Wolfram},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and
     Systems (IROS)},
  year = {2016},
  title = {Monocular Camera Localization in 3D LiDAR Maps}
}