Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localization

Difficulties in capturing unique structures in the outdoor environment hinders the map-based Autonomous Vehicles (AV) localization performance. Accordingly, this necessitates the use of high resolution sensors to capture more information from the environment. However, this approach is costly and limits the mass deployment of AV. To overcome this drawback, in this paper, we propose a novel outdoor map-based localization method for Autonomous Vehicles in urban environments using sparse 3D lidar scan data. In the proposed method, a Point-to-Distribution (P2D) formulation of the Normal Distributions Transform (NDT) approach is applied in a Monte Carlo Localization (MCL) framework. The formulation improves the measurement model of localization by taking individual lidar point measurements into consideration. Additionally, to apply the localization to scalable outdoor environments, a flexible and efficient map structure is implemented. The experimental results indicate that the proposed approach significantly improves the localization and its robustness in outdoor AV environments, especially with limited sparse lidar data.

[1]  Hao Wang,et al.  Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Edilson de Aguiar,et al.  A light-weight yet accurate localization system for autonomous cars in large-scale and complex environments , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

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

[4]  Paul Newman,et al.  Real-time probabilistic fusion of sparse 3D LIDAR and dense stereo , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[5]  Edilson de Aguiar,et al.  Large-scale mapping in complex field scenarios using an autonomous car , 2016, Expert Syst. Appl..

[6]  Ryan M. Eustice,et al.  Robust LIDAR localization using multiresolution Gaussian mixture maps for autonomous driving , 2017, Int. J. Robotics Res..

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

[8]  Sebastian Thrun,et al.  Map-Based Precision Vehicle Localization in Urban Environments , 2007, Robotics: Science and Systems.

[9]  Grzegorz Cielniak,et al.  Semantic-assisted 3D normal distributions transform for scan registration in environments with limited structure , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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

[11]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[12]  Alberto Elfes,et al.  Real-time autonomous ground vehicle navigation in heterogeneous environments using a 3D LiDAR , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[13]  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).

[14]  Paul Newman,et al.  Leveraging experience for large-scale LIDAR localisation in changing cities , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Andreas Birk,et al.  Beyond points: Evaluating recent 3D scan-matching algorithms , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Liam Paull,et al.  Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

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

[18]  Hyun Myung,et al.  Robust Vehicle Localization Using Entropy-Weighted Particle Filter-based Data Fusion of Vertical and Road Intensity Information for a Large Scale Urban Area , 2017, IEEE Robotics and Automation Letters.

[19]  Jari Saarinen,et al.  Normal distributions transform Monte-Carlo localization (NDT-MCL) , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Wei Tian,et al.  Research on Localization Vehicle Based on Multiple Sensors Fusion System , 2017, 2017 International Conference on Computer Network, Electronic and Automation (ICCNEA).