Abstract
Autonomous driving technology has become a spotlight in recent years. Of all the factors related to autonomous driving, safety should be first considered. A safe global trajectory should be planned at beginning and local safe trajectory should be planned according to the situations in real time. Due to this, the intelligent vehicles must know where they are in real time to do the next control steps. In this paper, a high-precision localization framework for intelligent vehicles is proposed. A vertical low-cost LIDAR is used for mapping and live data collection. High-precision maps are generated by projecting laser scans along the survey trajectory produced by trajectory filter. When localizing, an improved matching method particle Iterative Closet Point is proposed. Using this particle ICP, not only the matching precision is improved, but also the computing time decreases remarkably, which helps to make the algorithm real-time. Decimeter-level precision can be achieved by the validation of experiments. The results show much benefit for safe driving by this Monte Carlo framework.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Levinson, J., Thrun, S.: Robust vehicle localization in urban environments using probabilistic maps. In: 2010 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2010)
Sheehan, M., Harrison, A., Newman, P.: Continuous vehicle localisation using sparse 3d sensing, kernelised réyi distance and fast gauss transforms. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2013)
Baldwin, I., Newman, P.: Road vehicle localization with 2D push-broom LIDAR and 3D priors. In: 2012 IEEE international conference on Robotics and automation (ICRA). IEEE (2012)
Chong, Z.J., et al.: Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment. In: 2013 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2013)
Maddern, W., Pascoe, G., Newman, P.: Leveraging experience for large-scale LIDAR localisation in changing cities. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2015)
Fujii, K.: Extended Kalman Filter. Refernce Manual (2013)
Del Moral, P.: Non-linear filtering: interacting particle resolution. Markov Process. Relat. Fields 2(4), 555–581 (1996)
Del Moral, P.: Feynman-Kac formulae. Genealogical and interacting particle approximations (2004)
Best, P.J., McKay, Neil D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)
Liu, Z., et al.: Action selection for active and cooperative global localization based on localizability estimation. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE (2014)
Acknowledgements
This work was supported by the National Natural Science Foundation of China(91420101), International Chair on automated driving of ground vehicle, National Magnetic Confinement Fusion Science Program(2012GB102002).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, L., Yang, M., Guo, L., Wang, C., Wang, B. (2017). Precise and Reliable Localization of Intelligent Vehicles for Safe Driving. In: Chen, W., Hosoda, K., Menegatti, E., Shimizu, M., Wang, H. (eds) Intelligent Autonomous Systems 14. IAS 2016. Advances in Intelligent Systems and Computing, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-319-48036-7_81
Download citation
DOI: https://doi.org/10.1007/978-3-319-48036-7_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48035-0
Online ISBN: 978-3-319-48036-7
eBook Packages: EngineeringEngineering (R0)