Adaptive Resolution Refinement of NDT Map Based on Localization Error Modeled by Map Factors

One of the prominent methods for accurate self-localization for autonomous vehicles is map-matching with light detection and ranging (LiDAR) based on Normal distribution transform (NDT). In NDT, map space is divided into the grids, and for each grid, normal distribution (ND) of the points are calculated, and LiDAR scan is matched to these NDs. Bigger grid sizes (lower resolution) are more favorable because it can abstract more points in each grid and reduce map size. However, if the resolution is low, many details of the environment are ignored, and the localization accuracy degrades. This information loss and localization error is different from place to place on the map and can be evaluated beforehand for each resolution. In this work, ten map factors are used to evaluate the localization ability of the map in a specific position for each resolution. Using the evaluation result, for each position of the map, a lower resolution that can preserve the required localization accuracy are determined. In this method, NDT map is generated by adaptively selecting the resolution for each position of the map. Experimental results in Shinjuku, Tokyo, show that by using this strategy, map size can be reduced by up to 32% of the original size while the mean localization error remains less than 0.141m.

[1]  Takashi Tsubouchi,et al.  A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Nicolas David,et al.  IMPROVING 3D LIDAR POINT CLOUD REGISTRATION USING OPTIMAL NEIGHBORHOOD KNOWLEDGE , 2012 .

[3]  Yanlei Gu,et al.  Autonomous vehicle self-localization based on multilayer 2D vector map and multi-channel LiDAR , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[4]  Martin Magnusson,et al.  The three-dimensional normal-distributions transform : an efficient representation for registration, surface analysis, and loop detection , 2009 .

[5]  Joachim Hertzberg,et al.  Evaluation of 3D registration reliability and speed - A comparison of ICP and NDT , 2009, 2009 IEEE International Conference on Robotics and Automation.

[6]  Eijiro Takeuchi,et al.  Compressing continuous point cloud data using image compression methods , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[7]  Tao Mei,et al.  Lidar Scan matching EKF-SLAM using the differential model of vehicle motion , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[8]  Ji Zhang,et al.  LOAM: Lidar Odometry and Mapping in Real-time , 2014, Robotics: Science and Systems.

[9]  Yanlei Gu,et al.  Factors to Evaluate Capability of Map for Vehicle Localization , 2018, IEEE Access.

[10]  Basilio Bona,et al.  Active SLAM and Exploration with Particle Filters Using Kullback-Leibler Divergence , 2014, J. Intell. Robotic Syst..

[11]  Eijiro Takeuchi,et al.  Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[12]  Wolfram Burgard,et al.  Large scale graph-based SLAM using aerial images as prior information , 2009, Auton. Robots.

[13]  Tom Duckett,et al.  Scan registration for autonomous mining vehicles using 3D‐NDT , 2007, J. Field Robotics.

[14]  Gamini Dissanayake,et al.  Fast global scan matching for high-speed vehicle navigation , 2015, 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[15]  Christoph Stiller,et al.  Velodyne SLAM , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[16]  Peter Biber,et al.  The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[17]  Jaebum Choi,et al.  Hybrid map-based SLAM using a Velodyne laser scanner , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).