Pose And Motion From Omnidirectional Optical Flow And a Digital Terrain Map
.
Pose and Motion from Omnidirectional Optical Flow and a Digital Terrain Map.
In Proc. Of the IEEE/RSJ International Conf. on Intelligent Robots and Systems, 2006
Online Version
A pdf version is available for download.
Abstract
An algorithm for pose and motion estimation using corresponding features in omnidirectional images and a digital terrain map is proposed. In previous paper, such algorithm for regular camera was considered. Using a Digital Terrain (or Digital Elevation) Map (DTM/DEM) as a global reference enables recovering the absolute position and orientation of the camera. In order to do this, the DTM is used to formulate a constraint between corresponding features in two consecutive frames. In this paper, these constraints are extended to handle non-central projection, as is the case with many omnidirectional systems. The utilization of omnidirectional data is shown to improve the robustness and accuracy of the navigation algorithm. The feasibility of this algorithm is established through lab experimentation with two kinds of omnidirectional acquisition systems. The first one is polydioptric cameras while the second is catadioptric camera.
Co-authors
Bibtex Entry
@inproceedings{LernerKR06i,
title = {Pose and Motion from Omnidirectional Optical Flow and a Digital Terrain Map.},
author = {Ronen Lerner and Oleg Kupervasser and Ehud Rivlin},
year = {2006},
month = {October},
booktitle = {Proc. Of the IEEE/RSJ International Conf. on Intelligent Robots and Systems},
abstract = {An algorithm for pose and motion estimation using corresponding features in omnidirectional images and a digital terrain map is proposed. In previous paper, such algorithm for regular camera was considered. Using a Digital Terrain (or Digital Elevation) Map (DTM/DEM) as a global reference enables recovering the absolute position and orientation of the camera. In order to do this, the DTM is used to formulate a constraint between corresponding features in two consecutive frames. In this paper, these constraints are extended to handle non-central projection, as is the case with many omnidirectional systems. The utilization of omnidirectional data is shown to improve the robustness and accuracy of the navigation algorithm. The feasibility of this algorithm is established through lab experimentation with two kinds of omnidirectional acquisition systems. The first one is polydioptric cameras while the second is catadioptric camera.}
}