Ego-Motion Recovery and Robust Tilt Estimation for Planar Motion Using Several Homographies

Wadenbäck, Mårten; Heyden, Anders (2014). Ego-Motion Recovery and Robust Tilt Estimation for Planar Motion Using Several Homographies. Battiato, Sebastiano; Braz, José (Eds.). 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2014), Proceedings of, 635 - 639. 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2014). Lisbon, Portugal: SciTePress
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DOI:
Conference Proceeding/Paper | Published | English
Authors:
Wadenbäck, Mårten ; Heyden, Anders
Editors:
Battiato, Sebastiano ; Braz, José
Department:
Mathematics (Faculty of Engineering)
Mathematical Imaging Group
ELLIIT: the Linköping-Lund initiative on IT and mobile communication
eSSENCE: The e-Science Collaboration
Project:
ENGROSS
Research Group:
Mathematical Imaging Group
Abstract:
In this paper we suggest an improvement to a recent algorithm for estimating the pose and ego-motion of a camera which is constrained to planar motion at a constant height above the floor, with a constant tilt. Such motion is common in robotics applications where a camera is mounted onto a mobile platform and directed towards the floor. Due to the planar nature of the scene, images taken with such a camera will be related by a planar homography, which may be used to extract the ego-motion and camera pose. Earlier algorithms for this particular kind of motion were not concerned with determining the tilt of the camera, focusing instead on recovering only the motion. Estimating the tilt is a necessary step in order to create a rectified map for a SLAM system. Our contribution extends the aforementioned recent method, and we demonstrate that our enhanced algorithm gives more accurate estimates of the motion parameters.
Keywords:
SLAM ; Homography ; Robotic Navigation ; Planar Motion ; Tilt Estimation ; Computer graphics and computer vision ; Mathematical Sciences
LUP-ID:
72c8b14d-3913-4111-a334-3ea7646bd7ea | Link: https://lup.lub.lu.se/record/72c8b14d-3913-4111-a334-3ea7646bd7ea | Statistics

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