Decentralized Localization Framework using Heterogeneous Map-matchings
Pages 2183 - 2189
Abstract
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.
References
[1]
Suhr, J. K., Jang, J., Min, D. and Jung, H. G. (2017). Sensor fusion-based low-cost vehicle localization system for complex urban environments. IEEE Transactions on Intelligent Transportation Systems, 18(5), 1078–1086.
[2]
Mur-Artal, R., Montiel, J.M.M. and Tardos, J.D., 2015. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Transactions on Robotics, 31(5), pp. 1147–1163.
[3]
Engel, Jakob, Jorg Stckler, and Daniel Cremers. “Large-scale direct SLAM with stereo cameras.” Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. IEEE, 2015.
[4]
Levinson, Jesse, and Sebastian Thrun. “Robust vehicle localization in urban environments using probabilistic maps.” Robotics and Automation (ICRA), 2010 IEEE International Conference on. IEEE, 2010.
[5]
Liang Li, Ming Yang, Chunxiang Wang, and Bing Wang, “Hybrid Filtering Framework based Robust Localization for Industrial Vehicles,” IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018.
[6]
Hyunsung Lee, Seonwook Kim, Sungyoul Park, Yonghwan Jeong, Hojoon Lee, and Kyongsu Yi, “AVM / LIDAR Sensor based Lane Marking Detection Method for Automated Driving on Complex Urban Roads,” Intelligent Vehicles Symposium (IV), 2015 IEEE. IEEE, 2017.
[7]
Zhang, Ji, and Sanjiv Singh. “Visual-lidar odometry and mapping: Low-drift, robust, and fast.” in Robotics and Automation (ICRA), 2015 IEEE International Conference on, pp. 2174–2181. IEEE, 2015.
[8]
De Silva, Varuna, Jamie Roche, and Ahmet Kondoz. “Fusion of Li-DAR and Camera Sensor Data for Environment Sensing in Driverless Vehicles.” arXiv preprint arXiv: (2017).
[9]
Zhang, Xinzheng, Ahmad B. Rad, and Yiu-Kwong Wong. “Sensor fusion of monocular cameras and laser range-finders for line-based simultaneous localization and mapping (SLAM) tasks in autonomous mobile robots.” Sensors 12.1 (2012): 429–452.
[10]
Jo, Sungjin, Hyukdoo Choi, and Euntai Kim. “Ceiling vision based slam approach using sensor fusion of sonar sensor and monocular camera.” Control, Automation and Systems (ICCAS), 2012 12th International Conference on. IEEE, 2012.
[11]
Wan, Guowei et al., “Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes.” arXiv preprint arXiv: (2017).
[12]
Kim, H., Liu, B., Goh, C. Y., Lee, S. and Myung, H.(2017). Robust Vehicle Localization Using Entropy-Weighted Particle Filter-based Data Fusion of Vertical and Road Intensity Information for a Large Scale Urban Area. IEEE Robotics and Automation Letters, 2(3), 1518–1524.
[13]
Xie, Saining, and Zhuowen Tu. “Holistically-nested edge detection.” Proceedings of the IEEE international conference on computer vision. 2015.
[14]
Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv: (2014).
[15]
Lee, Soomok et al., “Directional-DBSCAN: Parking-slot detection using a clustering method in around-view monitoring system.” Intelligent Vehicles Symposium (IV), 2016 IEEE. IEEE, 2016.
[16]
Lee, Soomok, Seong-Woo Kim, and Seung-Woo Seo. “Accurate ego-lane recognition utilizing multiple road characteristics in a Bayesian network framework.” Intelligent Vehicles Symposium (IV), 2015 IEEE. IEEE, 2015.
[17]
Wolcott, R. W., & Eustice, R. M. (2015, May). Fast LIDAR localization using multiresolution Gaussian mixture maps. In Robotics and Automation (ICRA), 2015 IEEE International Conference on (pp. 2814–2821). IEEE.
[18]
Smets, Philippe, and Robert Kennes. “The transferable belief model.” Classic Works of the Dempster-Shafer Theory of Belief Functions. Springer, Berlin, Heidelberg, 2008. 693–736.
[19]
J. Hur, S. Kang and S. Seo. “Multi-lane detection in urban driving environments using conditional random fields.” in IEEE Intelligent Vehicles Symposium, Gold Coast, Austrailia, 2013.
[20]
Hyeon, D., Lee, S., Jung, S., Kim, S. W. and Seo, S. W. (2016, June). Robust road marking detection using convex grouping method in around-view monitoring system. In Intelligent Vehicles Symposium (IV), 2016 IEEE (pp. 1004–1009). IEEE.
[21]
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.
[22]
R. O. Chavez-Garcia and O. Aycard, “Multiple sensor fusion and classification for moving object detection and tracking,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 2, pp. 525–534, Feb 2016.
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Oct 2018
7818 pages
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IEEE Press
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Published: 01 October 2018
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