Robust LIDAR Localization for Autonomous Driving in Rain

This paper introduces a map-based localization method aiming to increase robustness in rainy conditions. This method utilizes two types of features: ground reflectivity features and vertical features extracted from 3D LIDAR scans and builds vehicle pose belief with two filters: a histogram filter and a particle filter. The posterior distributions from the two filters are integrated to estimate vehicle poses. This method exploits advantages of both features and filters, compensating respective weakness to deal with complex urban environments. Testing was performed in the fair and rainy weather. Road test results prove robustness and reliability of the proposed method.

[1]  Ryan M. Eustice,et al.  Fast LIDAR localization using multiresolution Gaussian mixture maps , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[3]  Frank Dellaert,et al.  iSAM2: Incremental smoothing and mapping using the Bayes tree , 2012, Int. J. Robotics Res..

[4]  Keisuke Yoneda,et al.  Improving localization accuracy for autonomous driving in snow-rain environments , 2016, 2016 IEEE/SICE International Symposium on System Integration (SII).

[5]  Emilio Frazzoli,et al.  Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment , 2013, 2013 IEEE International Conference on Robotics and Automation.

[6]  Claus Brenner,et al.  Global Localization of Vehicles Using Local Pole Patterns , 2009, DAGM-Symposium.

[7]  Wolfram Burgard,et al.  Map-Based Precision Vehicle Localization in Urban Environments , 2008 .

[8]  Claus Brenner,et al.  Extraction of Features from Mobile Laser Scanning Data for Future Driver Assistance Systems , 2009, AGILE Conf..

[9]  S. Kammel,et al.  Lidar-based lane marker detection and mapping , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[10]  Paul Newman,et al.  Laser-only road-vehicle localization with dual 2D push-broom LIDARS and 3D priors , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Wolfram Burgard,et al.  Monte Carlo localization in outdoor terrains using multilevel surface maps , 2008, J. Field Robotics.

[12]  Ji Zhang,et al.  Low-drift and real-time lidar odometry and mapping , 2017, Auton. Robots.

[13]  Thavida Maneewarn,et al.  ICP-EKF localization with adaptive covariance for a boiler inspection robot , 2015, 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[14]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[15]  Sebastian Thrun,et al.  Robust vehicle localization in urban environments using probabilistic maps , 2010, 2010 IEEE International Conference on Robotics and Automation.

[16]  Tao Wu,et al.  Vehicle localization using road markings , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[17]  H. González-Jorge,et al.  Quantifying the influence of rain in LiDAR performance , 2017 .

[18]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Gyu-In Jee,et al.  Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area , 2016, Sensors.

[20]  Wolfram Burgard,et al.  Autonomous driving in a multi-level parking structure , 2009, 2009 IEEE International Conference on Robotics and Automation.

[21]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.