Abstract
With the migration into automated driving for various classes of vehicles, affordable self-positioning upto at least cm accuracy is a goal to be achieved. Commonly used techniques such as GPS are either not accurate enough in their basic variant or accurate but too expensive. In addition, sufficient GPS coverage is in several cases not guaranteed. In this paper we propose positioning of a vehicle based on fusion of several sensor inputs. We consider inputs from improved GPS (with internet based corrections), inertia sensors and vehicle sensors fused with computer vision based positioning. For vision-based positioning, cameras are used for feature-based visual odometry to do relative positioning and beacon-based for absolute positioning. Visual features are brought into a dynamic map which allows sharing information among vehicles and allows us to deal with less robust feautures. This paper does not present final results, yet it is intended to share ideas that are currently being investigated and implemented.
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
Stavens, D.M.: Learning to drive: Perception for autonomous cars, tech. rep., Stanford University, Palo Alto, USA, May 2011
Scaramuzza, D., Fraundorfer, F.: Visual odometry, part 1: The first 30 years and fundamentals. IEEE Robotics & Automation Magazine, December 2011
Fraundorfer, F., Scaramuzza, D.: Visual odometry, part 2: Matching, robustness, optimization, and applications. IEEE Robotics & Automation Magazine, June 2012
Tiberius, C., van Bree, R., Buist, P.: Mapping motorway lanes and real-time lane identification with single-frequency precise point positioning. In: InsideGNSS, November/December 2011
Geiger, A., Ziegler, J., Stiller, C.: Stereoscan: Dense 3d reconstruction in real-time. In: Intelligent Vehicles Symposium 2011, Baden Baden, Germany, June 2011
Van Hamme, D., Veelaert, P., Philips, W.: Robust visual odometry using uncertainty models. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 1–12. Springer, Heidelberg (2011)
Steinhoff, U., Omerčević, D., Perko, R., Schiele, B., Leonardis, A.: How computer vision can help in outdoor positioning. In: Schiele, B., et al. (eds.) AMI 2007. LNCS, vol. 4794, pp. 124–141. Springer, Heidelberg (2007)
Badino, H., Huber, D., Kanade, T.: Visual topometric localization. In: Intelligent Vehicles Symposium 2011, Baden Baden, Germany, June 2011
Lategahn, H., Geiger, A., Kitt, B., Stiller, C.: Motion-without-structure: real-time multipose optimization for accurate visual odometry. In: Intelligent Vehicles Symposium 2012, Alcala de Henares, Spain, June 2012
Lategahn, H., Beck, J., Kitt, B., Stiller, C.: How to learn an illumination robust image feature for place recognition. In: IEEE Intelligent Vehicles Symposium 2013, Gold Coast, Australia, June 2013
S. Micro-Electronics: Inemo inertial module: 3d accelerometer, 3d gyroscope, 3d magnetometer. Datasheet (2013)
Yamamoto, Y., Pirjanian, P., Munich, M., DiBernardo, E., Goncalves, E., Ostrowsku, J., Karlsson, N.: Optical sensing for robot perception and localization. In: Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, June 2005
Olson, E.: Apriltag: a robust and flexible visual fiducial system. In: Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China, May 2011
uBlox: Neo-7p u-blox 7 precise point positioning gnss module. Datasheet (2014)
de Bakker, P., Knoop, V., Tiberius, C., van Arem, B.: Mapping motorway lanes and real-time lane identification with single-frequency precise point positioning. In: Proceedings of the Euroean Navigation Conference (ENC)-GNSS 2014, Rotterdam, The Netherlands, April 2014
Brubaker, M.A., Geiger, A., Urtasun, R.: Lost! leveraging the crowd for probabilistic visual self-localization. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Janssen, K., Rademakers, E., Boulkroune, B., El Ghouti, N., Kleihorst, R. (2015). Bootstrapping Computer Vision and Sensor Fusion for Absolute and Relative Vehicle Positioning. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-25903-1_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25902-4
Online ISBN: 978-3-319-25903-1
eBook Packages: Computer ScienceComputer Science (R0)