Decentralized Localization Framework using Heterogeneous Map-matchings

Highly accurate and robust real-time localization is an essential technique for various autonomous driving applications. Numerous localization methods have been proposed that combine various types of sensors, including an environmental sensor, IMU and GPS. However, the usage of a single environmental sensor is rather fragile. Although the use of multi-environment sensors is a better alternative, fusion methods from previous studies have not adequately compensated for shortcomings in dissimilar sensors or have not considered errors in the pre-built map. In this paper, we propose a decentralized localization framework using heterogeneous map-matching sources. Decentralized localization performs two independent map-matchings and integrates them with a stochastic situational analysis model. By applying a stochastic model, the reliability of the two map matchings is collected and system stability is verified. A number of experiments with autonomous vehicles within the actual driving environment have shown that combining multiple map-matching sources ensures more robust results than the use of a single environmental sensor.

[1]  Ho Gi Jung,et al.  Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments , 2017, IEEE Transactions on Intelligent Transportation Systems.

[2]  Ahmet M. Kondoz,et al.  Fusion of LiDAR and Camera Sensor Data for Environment Sensing in Driverless Vehicles , 2017, ArXiv.

[3]  Seung-Woo Seo,et al.  Directional-DBSCAN: Parking-slot detection using a clustering method in around-view monitoring system , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[4]  Euntai Kim,et al.  Ceiling vision based SLAM approach using sensor fusion of sonar sensor and monocular camera , 2012, 2012 12th International Conference on Control, Automation and Systems.

[5]  Xinzheng Zhang,et al.  Sensor Fusion of Monocular Cameras and Laser Rangefinders for Line-Based Simultaneous Localization and Mapping (SLAM) Tasks in Autonomous Mobile Robots , 2012, Sensors.

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

[7]  Alessandro Saffiotti,et al.  The Transferable Belief Model , 1991, ECSQARU.

[8]  Ricardo Omar Chávez García,et al.  Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking , 2016, IEEE Transactions on Intelligent Transportation Systems.

[9]  Seung-Woo Seo,et al.  Robust road marking detection using convex grouping method in around-view monitoring system , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[10]  Seung-Woo Seo,et al.  Accurate ego-lane recognition utilizing multiple road characteristics in a Bayesian network framework , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[11]  Ji Zhang,et al.  Visual-lidar odometry and mapping: low-drift, robust, and fast , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Yonghwan Jeong,et al.  AVM / LiDAR sensor based lane marking detection method for automated driving on complex urban roads , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

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

[14]  Seung-Woo Seo,et al.  Multi-lane detection in urban driving environments using conditional random fields , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[15]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[16]  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.

[17]  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).

[18]  Jörg Stückler,et al.  Large-scale direct SLAM with stereo cameras , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[19]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[20]  J. M. M. Montiel,et al.  ORB-SLAM: A Versatile and Accurate Monocular SLAM System , 2015, IEEE Transactions on Robotics.

[21]  Ming Yang,et al.  Hybrid Filtering Framework Based Robust Localization for Industrial Vehicles , 2018, IEEE Transactions on Industrial Informatics.