Abstract
In this paper, we present a new localization algorithm to estimate the localization of a robot based on prior data. Over the past decade, the emergence of numerous ways to utilize various prior data has opened up possibilities for their applications in robotics technologies. However, challenges still remain in estimating a robot’s 6-DoF position by simply analyzing the limited information provided by images from a robot. This paper describes a method of overcoming this technical hurdle by calculating the robot’s 6-DoF location. It only utilizes a current 2D image and prior data, which consists of its corresponding 3D point cloud and images, to calculate the 6-DoF position. Furthermore, we employed the SURF algorithm to find the robot’s position by using the image’s features and the 3D projection method. Experiments were conducted by the loop of 510m trajectory, which is included the prior data. It is expected that our method can be applied to broad areas by using enormous data such as point clouds and street views in the near future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kanayama, Y., Kimura, Y., Miyazaki, F., Noguchi, T.: A stable tracking control method for an autonomous mobile robot. In: Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 384–389 (1990)
Gage, D.W.: UGV history 101: A brief history of unmanned ground vehicle (UGV) development efforts. Unmanned Systems Magazine 13(3) (1995)
Obst, M., Bauer, S., Reisdorf, P., Wanielik, G.: Multipath detection with 3D digital maps for robust multi-constellation GNSS/INS vehicle localization in urban areas. In: Proc. of IEEE Intelligent Vehicles Symposium (IV), pp. 184–190 (2012)
Anguelov, D., Dulong, C., Filip, D., Frueh, C., Lafon, S., Lyon, R., Ogale, A., Vincent, L., Weaver, J.: Google street view: Capturing the world at street level. Computer 43(6), 32–38 (2010)
Saito, T., Kuroda, Y.: Mobile robot localization using multiple observations based on place recognition and gps. In: Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 1540–1545 (2013)
Baldwin, I., Newman, P.: Road vehicle localization with 2D push-broom lidar and 3D priors. In: Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 2611–2617 (2012)
Stewart, A.D., Newman, P.: Laps-localisation using appearance of prior structure: 6-DOF monocular camera localisation using prior pointclouds. In: Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 2625–2632 (2012)
Kim, H., Oh, T., Lee, D., Choe, Y., Chung, M.J., Myung, H.: Mobile Robot Localization by Matching 2D Image Features to 3D Point Cloud. In: Proc. of IEEE Int. Conf. on Ubiquitous Robots and Ambient Intelligence, pp. 266–267 (2013)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Kim, H., Lee, D., Oh, T., Lee, S.W., Choe, Y., Myung, H. (2014). Feature-Based 6-DoF Camera Localization Using Prior Point Cloud and Images. In: Kim, JH., Matson, E., Myung, H., Xu, P., Karray, F. (eds) Robot Intelligence Technology and Applications 2. Advances in Intelligent Systems and Computing, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-319-05582-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-05582-4_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05581-7
Online ISBN: 978-3-319-05582-4
eBook Packages: EngineeringEngineering (R0)