To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC) areas, such as roads with lanes, and loose constraint (LC) areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method.
References
[1]
Urmson, C.; Anhalt, J.; Bagnell, D.; Baker, C.; Bittner, R.; Clark, M.N.; Dolan, J.; Duggins, D.; Galatali, T.; Geyer, C.; et al. Autonomous driving in urban environments: Boss and the urban challenge. J. Field Robot 2008, 25, 425–466.
[2]
Montemerlo, M.; Becker, J.; Bhat, S.; Dahlkamp, H.; Dolgov, D.; Ettinger, S.; Haehnel, D.; Hilden, T.; Hoffmann, G.; Huhnke, B.; et al. Junior: The Stanford entry in the urban challenge. J. Field Robot 2008, 25, 569–597.
[3]
Fox, D.; Burgard, W.; Dellaert, F.; Thrun, S. Monte Carlo Localization: Efficient Position Estimation for Mobile Robots. Proceedings of the 1999 16th National Conference on Artificial Intelligence, Orlando, FL, USA, 18–22 July 1999; pp. 343–349.
[4]
Dellaert, F.; Fox, D.; Burgard, W.; Thrun, S. Monte Carlo Localization for Mobile Robots. Proceedings of 1999 International Conference on Robotics and Automation, Detroit, MI, USA, 10–15 May 1999; 2, pp. 1322–1328.
[5]
Burgard, W.; Derr, A.; Fox, D.; Cremers, A.B. Integrating Global Position Estimation and Position Tracking for Mobile Robots: The Dynamic Markov Localization Approach. Proceedings of 1998 International Conference on Intelligent Robots and Systems, Victoria, BC, Canada, 13–17 October 1998; 2, pp. 730–735.
[6]
Burgard, W.; Fox, D.; Hennig, D.; Schmidt, T. Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids. Proceedings of National Conference on Artificial Intelligence, Portland, OR, USA, 4–8 August 1996; 2, pp. 896–901.
[7]
Payá, L.; Fernández, L.; Gil, A.; Reinoso, O. Map building and monte carlo localization using global appearance of omnidirectional images. Sensors 2010, 10, 11468–11497.
[8]
Junichi, S. Adaptive slope filtering of airborne LiDAR data in urban areas for digital terrain model (DTM) generation. Remote Sens 2012, 4, 1804–1819.
[9]
Kyle, A.; Katheryn, I.L.; Willem, J.D. Fusion of high resolution aerial multispectral and LiDAR data: land cover in the context of urban mosquito habitat. Remote Sens 2011, 3, 2364–2383.
[10]
Lehtom？ki, M.; Jaakkola, A.; Hyypp？, J.; Kukko, A.; Kaartinen, H. Detection of vertical pole-like objects in a road environment using vehicle-based laser scanning data. Remote Sens 2010, 2, 641–664.
[11]
Howard, B. Google: Self-Driving Cars in 3–5 Years. Feds: Not So Fast, Available online: http://www.extremetech.com/extreme/147940-google-self-driving-cars-in-3-5-years-feds-not-so-fast (accessed on 8 April 2013).
[12]
Guizzo, E. How Google’s Self-Driving Car Works. Available online: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works (accessed on 18 October 2011).
[13]
Xiong, G.; Zhou, P.; Zhou, S.; Zhao, X.; Zhang, H.; Gong, J.; Chen, H. Autonomous Driving of Intelligent Vehicle BIT in 2009 Future Challenge of China. Proceedings of 2010 IEEE Intelligent Vehicles Symposium, San Diego, NJ, USA, 21–24 June 2010; pp. 1049–1053.
[14]
Haklay, M.; Weber, P. Openstreetmap: user-generated street maps. IEEE Perv. Comput 2008, 7, 12–18.
[15]
Gil, A.; Reinoso, ó.; Ballesta, M.; Juliá, M.; Payá, L. Estimation of visual maps with a robot network equipped with vision sensors. Sensors 2010, 10, 5209–5232.
[16]
He, B.; Zhang, S.; Yan, T.; Zhang, T.; Liang, Y.; Zhang, H. A novel combined SLAM based on RBPF-SLAM and EIF-SLAM for mobile system sensing in a large scale environment. Sensors 2011, 11, 10197–10219.
[17]
Zhang, X.; Rad, A.B.; Wong, Y. Sensor fusion of monocular cameras and laser rangefinders for line-based Simultaneous Localization and Mapping (SLAM) tasks in autonomous mobile robots. Sensors 2012, 12, 429–452.
[18]
Shi, Y.; Ji, S.; Shi, Z.; Duan, Y.; Shibasaki, R. GPS-supported visual SLAM with a rigorous sensor model for a panoramic camera in outdoor environments. Sensors 2012, 13, 119–136.
[19]
Zhao, H.; Chiba, M.; Shibasaki, R.; Shao, X.; Cui, J.; Zha, H. SLAM in a Dynamic Large Outdoor Environment Using a Laser Scanner. Proceedings of 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, 19–23 May 2008; pp. 1455–1462.
[20]
Trung-Dung, V.; Burlet, J.; Aycard, O. Grid-based localization and local mapping with moving object detection and tracking. Inform. Fusion 2011, 12, 58–69.
[21]
Weiss, T.; Schiele, B.; Dietmayer, K. Robust Driving Path Detection in Urban and Highway Scenarios Using a Laser Scanner and Online Occupancy Grids. Proceedings of 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, 13–15 June 2007; pp. 184–189.
[22]
Zhou, S.; Jiang, Y.; Gong, J.; Xiong, G.; Chen, H. A Novel Lane Detection Based on Geometrical Model and Gabor Filter. Proceedings of 2010 IEEE Intelligent Vehicles Symposium, San Diego, CA, USA, 21–24 June 2010; pp. 59–64.
[23]
Gong, J.; Jiang, Y.; Xiong, G.; Guan, C.; Tao, G.; Chen, H. The Recognition and Tracking of Traffic Lights Based on Color Segmentation and CAMSHIFT for Intelligent Vehicles. Proceedings of 2010 IEEE Intelligent Vehicles Symposium, San Diego, CA, USA, 21–24 June 2010; pp. 431–435.
[24]
Jiang, Y.; Zhou, S.; Jiang, Y.; Gong, J.; Xiong, G.; Chen, H. Traffic Sign Recognition Using Ridge Regression and OTSU Method. Proceedings of 2011 IEEE Intelligent Vehicles Symposium, Baden-Baden, Germany, 5–9 June 2011; pp. 613–618.
[25]
Rowe, A.; Starr, Z.; Rajkumar, R. Using Micro-Climate Sensing to Enhance RF Localization in Assisted Living Environments. Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Montreal, QC, Canada, 7–10 October 2007; pp. 3368–3375.
[26]
Fox, D. Markov Localization: A Probabilistic Frame for Mobile Robot Localization and NavigationPh.D. Thesis. University of Bonn, Bonn, Germany, 1998.
[27]
Jiang, Y.; Zhao, S.; Gong, J.; Xiong, G.; Chen, H. System design of self-driving in simplified urban environments (in Chinese). Chin. J. Mech. Eng 2012, 20, 103–112.
[28]
Azimi, S.R.; Bhatia, G.; Rajkumar, R.; Mudalige, P. Vehicular networks for collision avoidance at intersections. SAE Inter. J. Passenger Car.-Mech. Syst 2011, 4, 406–416.
