research-article

Real-Time Applications Using High Resolution 3D Objects in High Definition Maps (Systems Paper)

Authors:

Goce TrajcevskiAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 229 - 238

https://doi.org/10.1145/3347146.3359353

Published: 05 November 2019 Publication History

Abstract

One of the greatest challenges in automated driving is the ability to acquire, access and query the data pertaining to high resolution 3D objects from multiple heterogeneous sources. Specifically, the information extraction needs to be done by fusing data from both sensors and databases, and with real-time constraints. Existing structures and algorithmic approaches designed for regular maps - or even regular features in High Definition maps - are not optimal to handle the various challenges. In this paper, we review the importance and roles of high resolution 3D objects in High Definition maps being used in autonomous driving applications and summarize the characteristics of 3D objects compared to other regular map features. We also describe an end-to-end pipeline of a system targeting such problems and emphasize the challenges and feasible solutions to each part of the pipeline. Last but not least, we define the quantified evaluation metrics for each task and introduce the dataset that we built for this objective.

References

[1]

H-K Ahn, Nikos Mamoulis, and Ho Min Wong. A survey on multidimensional access methods. 2001.

[2]

Hernán Badino, Uwe Franke, and Rudolf Mester. Free space computation using stochastic occupancy grids and dynamic programming. In Workshop on Dynamical Vision, ICCV, Rio de Janeiro, Brazil, volume 20. Citeseer, 2007.

[3]

Ioan Andrei Barsan, Shenlong Wang, Andrei Pokrovsky, and Raquel Urtasun. Learning to localize using a lidar intensity map. In CoRL, pages 605--616, 2018.

[4]

Sven Bauer, Yasamin Alkhorshid, and Gerd Wanielik. Using high-definition maps for precise urban vehicle localization. In Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on, pages 492--497. IEEE, 2016.

[5]

Claus Brenner. Extraction of features from mobile laser scanning data for future driver assistance systems. In Advances in GIScience, pages 25--42. Springer, 2009.

[6]

Claus Brenner. Global localization of vehicles using local pole patterns. In Joint Pattern Recognition Symposium, pages 61--70. Springer, 2009.

[7]

Claus Brenner. Vehicle localization using landmarks obtained by a lidar mobile mapping system. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences:[PCV 2010-Photogrammetric Computer Vision And Image Analysis, Pt I] 38 (2010), Nr. Part 3A, 38(Part 3A):139--144, 2010.

[8]

Guillaume Bresson, Zayed Alsayed, Li Yu, and Sébastien Glaser. Simultaneous localization and mapping: A survey of current trends in autonomous driving. IEEE Transactions on Intelligent Vehicles, 2(3):194--220, 2017.

[9]

United States Census Bureau. Urban and rural, 2010.

[10]

Carl Byers and Andrew Woo. 3d data visualization: The advantages of volume graphics and big data to support geologic interpretation. Interpretation, 3(3):SX29--SX39, 2015.

[11]

David Coeurjolly, Serge Miguet, and Laure Tougne. 2d and 3d visibility in discrete geometry: an application to discrete geodesic paths. Pattern Recognition Letters, 25(5):561--570, 2004.

Digital Library

[12]

Jonathan D Cohen, Daniel G Aliaga, and Weiqiang Zhang. Hybrid simplification: combining multi-resolution polygon and point rendering. In Proceedings of the Conference on Visualization'01, pages 37--44. IEEE Computer Society, 2001.

[13]

Bertram Drost, Markus Ulrich, Nassir Navab, and Slobodan Ilic. Model globally, match locally: Efficient and robust 3d object recognition. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 998--1005. Ieee, 2010.

[14]

Guillermo Duenas Arana, Mathieu Joerger, and Matthew Spenko. Local nearest neighbor integrity risk evaluation for robot navigation. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 2328--2333. IEEE, 2018.

[15]

Guillermo Duenas Arana, Mathieu Joerger, and Matthew Spenko. Local nearest neighbor integrity risk evaluation for robot navigation. pages 2328--2333, 05 2018.

[16]

Benjamin Eckart. Compact Generative Models of Point Cloud Data for 3D Perception. PhD thesis, Carnegie Mellon University Pittsburgh, 2017.

[17]

Scott Gebhardt, Eliezer Payzer, Leo Salemann, A Fettinger, E Rotenberg, and C Seher. Polygons, point-clouds and voxels: A comparison of high-fidelity terrain representations. In Simulation Interoperability Workshop and Special Workshop on Reuse of Environmental Data for SimulationâĂŤProcesses, Standards, and Lessons Learned, 2009.

[18]

Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3354--3361. IEEE, 2012.

[19]

Farouk Ghallabi, Fawzi Nashashibi, Ghayath El-Haj-Shhade, and Marie-Anne Mittet. Lidar-based lane marking detection for vehicle positioning in an hd map. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2209--2214. IEEE, 2018.

[20]

Brian Guenter, Mark Finch, Steven Drucker, Desney Tan, and John Snyder. Foveated 3d graphics. ACM Transactions on Graphics (TOG), 31(6):164, 2012.

[21]

J-S Gutmann, Wolfram Burgard, Dieter Fox, and Kurt Konolige. An experimental comparison of localization methods. In Intelligent Robots and Systems, 1998. Proceedings., 1998 IEEE/RSJ International Conference on, volume 2, pages 736--743. IEEE, 1998.

[22]

Jens-Steffen Gutmann, Thilo Weigel, and Bernhard Nebel. Fast, accurate, and robust self-localization in polygonal environments. In IROS, pages 1412--1419, 1999.

[23]

Alberto Hata and Denis Wolf. Road marking detection using lidar reflective intensity data and its application to vehicle localization. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 584--589. IEEE, 2014.

[24]

Alberto Y Hata, Fernando S Osorio, and Denis F Wolf. Robust curb detection and vehicle localization in urban environments. In Intelligent Vehicles Symposium Proceedings, 2014 IEEE, pages 1257--1262. IEEE, 2014.

[25]

Alberto Y Hata and Denis F Wolf. Feature detection for vehicle localization in urban environments using a multilayer lidar. IEEE Transactions on Intelligent Transportation Systems, 17(2):420--429, 2015.

Digital Library

[26]

Darin Wayne Higgins and Dan Martin Scott. System and method for synchronizing raster and vector map images, January 9 2007. US Patent 7,161,604.

[27]

Wolfgang Hugemann. Driver reaction times in road traffic. In Proceedings of XI EVU (European Association for Accident Research and Accident Analysis) Annual Meeting. Portorož, Slovenija, volume 32, 2002.

[28]

Jun-Hyuck Im, Sung-Hyuck Im, and Gyu-In Jee. Vertical corner feature based precise vehicle localization using 3d lidar in urban area. Sensors, 16(8):1268, 2016.

[29]

Ehsan Javanmardi, Yanlei Gu, Mahdi Javanmardi, and Shunsuke Kamijo. Autonomous vehicle self-localization based on abstract map and multi-channel lidar in urban area. IATSS research, 43(1):1--13, 2019.

[30]

Jinyong Jeong, Younggun Cho, and Ayoung Kim. Road-slam: Road marking based slam with lane-level accuracy. In 2017 IEEE Intelligent Vehicles Symposium (IV), pages 1736--1473. IEEE, 2017.

[31]

Bing Jian and Baba C Vemuri. A robust algorithm for point set registration using mixture of gaussians. In Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, volume 2, pages 1246--1251. IEEE, 2005.

[32]

Bing Jian and Baba C Vemuri. Robust point set registration using gaussian mixture models. IEEE transactions on pattern analysis and machine intelligence, 33(8):1633--1645, 2011.

[33]

Gunnar Johansson and Kåre Rumar. Drivers' brake reaction times. Human factors, 13(1):23--27, 1971.

[34]

Andrew E Johnson and Martial Hebert. Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Transactions on Pattern Analysis & Machine Intelligence, (5):433--449, 1999.

Digital Library

[35]

Arne Kesting and Martin Treiber. How reaction time, update time, and adaptation time influence the stability of traffic flow. Computer-Aided Civil and Infrastructure Engineering, 23(2):125--137, 2008.

[36]

Ravi Kanth V Kothuri, Siva Ravada, and Daniel Abugov. Quadtree and r-tree indexes in oracle spatial: a comparison using gis data. In Proceedings of the 2002 ACM SIGMOD international conference on Management of data, pages 546--557. ACM, 2002.

Digital Library

[37]

Matti Lehtomäki, Anttoni Jaakkola, Juha Hyyppä, Antero Kukko, and Harri Kaartinen. Detection of vertical pole-like objects in a road environment using vehicle-based laser scanning data. Remote Sensing, 2(3):641--664, 2010.

[38]

Liang Li, Ming Yang, Lindong Guo, Chunxiang Wang, and Bing Wang. Precise and reliable localization of intelligent vehicles for safe driving. In International Conference on Intelligent Autonomous Systems, pages 1103--1115. Springer, 2016.

[39]

Liang Li, Ming Yang, Chunxiang Wang, and Bing Wang. Road dna based localization for autonomous vehicles. In Intelligent Vehicles Symposium (IV), 2016 IEEE, pages 883--888. IEEE, 2016.

[40]

Lars Linsen. Point cloud representation. Univ., Fak. für Informatik, Bibliothek Technical Report, Faculty of Computer Science, University of Karlsruhe, 2001.

[41]

Hang Liu, Qin Ye, Hairui Wang, Liang Chen, and Jian Yang. A precise and robust segmentation-based lidar localization system for automated urban driving. Remote Sensing, 11(11):1348, 2019.

[42]

Ellon Mendes, Pierrick Koch, and Simon Lacroix. Icp-based pose-graph slam. In 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages 195--200. IEEE, 2016.

Digital Library

[43]

Fabrice Monnier, Bruno Vallet, and Bahman Soheilian. Trees detection from laser point clouds acquired in dense urban areas by a mobile mapping system. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci, 3:245--250, 2012.

[44]

Jeffrey J Morrell. Estimated service life of wood poles. Technical Bulletin, North American Wood Pole Council, http://www.woodpoles.org/documents/TechBulletin_EstimatedServiceLifeofWoodPole_12-08. pdf (Last accessed 5 April 2013), 2008.

[45]

Department of Transportation MN. Pavement markings on challenging surfaces. https://www.dot.state.mn.us/trafficeng/safety/docs/pavementmarkings.pdf, 2007.

[46]

U.S. Department of Transportation Federal Highway Administration Office of Highway Policy Information. Public road length - 2013, miles by functional system, 2014.

[47]

Michael J Olsen, Christopher Parrish, Erzhuo Che, Jaehoon Jung, Joseph Greenwood, et al. Lidar for maintenance of pavement reflective markings and retrore-flective signs. Technical report, Oregon. Dept. of Transportation, 2018.

[48]

Clark F Olson. Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation, 16(1):55--66, 2000.

[49]

Joel Pazhayampallil and Kai Yuan Kuan. Deep learning lane detection for autonomous vehicle localization. 2013.

[50]

Ali Ufuk Peker, Oguz Tosun, and Tankut Acarman. Particle filter vehicle localization and map-matching using map topology. In 2011 IEEE Intelligent Vehicles Symposium (IV), pages 248--253. IEEE, 2011.

[51]

Oliver Pink and Christoph Stiller. Automated map generation from aerial images for precise vehicle localization. In Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, pages 1517--1522. IEEE, 2010.

[52]

Jann Poppinga, Narunas Vaskevicius, Andreas Birk, and Kaustubh Pathak. Fast plane detection and polygonalization in noisy 3d range images. In IROS. IEEE, 2008.

[53]

Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 1(2):4, 2017.

[54]

Xiaozhi Qu, Bahman Soheilian, and Nicolas Paparoditis. Vehicle localization using mono-camera and geo-referenced traffic signs. In Intelligent Vehicles Symposium (IV), 2015 IEEE, pages 605--610. IEEE, 2015.

[55]

Eike Rehder and Alexander Albrecht. Submap-based slam for road markings. In 2015 IEEE Intelligent Vehicles Symposium (IV), pages 1393--1398. IEEE, 2015.

[56]

Fabio Remondino. From point cloud to surface: the modeling and visualization problem. International Archives of photogrammetry, Remote Sensing and spatial information sciences, 34, 2003.

[57]

Fred Rothganger, Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. 3d object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. International Journal of Computer Vision, 66(3):231--259, 2006.

Digital Library

[58]

Alexander Schaefer, Daniel Büscher, Johan Vertens, Lukas Luft, and Wolfram Burgard. Long-term urban vehicle localization using pole landmarks extracted from 3-d lidar scans, 2019.

[59]

Claus Scheiblauer and Michael Wimmer. Out-of-core selection and editing of huge point clouds. Computers & Graphics, 35(2):342--351, 2011.

Digital Library

[60]

Alexander Schlichting and Claus Brenner. Localization using automotive laser scanners and local pattern matching. In Intelligent Vehicles Symposium Proceedings, 2014 IEEE, pages 414--419. IEEE, 2014.

[61]

Johannes L Schönberger, Marc Pollefeys, Andreas Geiger, and Torsten Sattler. Semantic visual localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6896--6906, 2018.

[62]

Markus Schreiber, Carsten Kn@oppel, and Uwe Franke. Laneloc: Lane marking based localization using highly accurate maps. In Intelligent Vehicles Symposium (IV), 2013 IEEE, pages 449--454. IEEE, 2013.

[63]

Markus Schütz. Potree: Rendering large point clouds in web browsers. Technische Universität Wien, Wiedeń, 2016.

[64]

Mohsen Sefati, M Daum, B Sondermann, Kai D Kreisköther, and Achim Kampker. Improving vehicle localization using semantic and pole-like landmarks. In 2017 IEEE Intelligent Vehicles Symposium (IV), pages 13--19. IEEE, 2017.

Digital Library

[65]

Heiko G Seif and Xiaolong Hu. Autonomous driving in the icityâĂŤhd maps as a key challenge of the automotive industry. Engineering, 2(2):159--162, 2016.

[66]

Zvi Shiller and Y-R Gwo. Dynamic motion planning of autonomous vehicles. IEEE Transactions on Robotics and Automation, 7(2):241--249, 1991.

[67]

Kumares C Sinha and Tien F Fwa. On the concept of total highway management. Transportation Research Record, 1229:79--88, 1989.

[68]

Richard Socher, Brody Huval, Bharath Bath, Christopher D Manning, and Andrew Y Ng. Convolutional-recursive deep learning for 3d object classification. In Advances in neural information processing systems, pages 656--664, 2012.

Digital Library

[69]

Navigation Data Standard. Navigation data standard - open lane model documentation. Technical report, Navigation Data Standard, 2016.

[70]

Hartmut Surmann, Andreas Nüchter, and Joachim Hertzberg. An autonomous mobile robot with a 3d laser range finder for 3d exploration and digitalization of indoor environments. Robotics and Autonomous Systems, 45(3-4):181--198, 2003.

[71]

PD Thompson, KM Ford, MHR Arman, S Labi, K Sinha, and A Shirolé. Nchrp report 713: Estimating life expectancies of highway assets. Transportation Research Board of the National Academies, Washington, DC, 2012.

[72]

TomTom. Tomtom hd map with roaddna, 2017.

[73]

Thorsten Weiss, Nico Kaempchen, and Klaus Dietmayer. Precise ego-localization in urban areas using laserscanner and high accuracy feature maps. In Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, pages 284--289. IEEE, 2005.

[74]

Andre Welzel, Pierre Reisdorf, and Gerd Wanielik. Improving urban vehicle localization with traffic sign recognition. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pages 2728--2732. IEEE, 2015.

Digital Library

[75]

Lihong Weng, Ming Yang, Lindong Guo, Bing Wang, and Chunxiang Wang. Pole-based real-time localization for autonomous driving in congested urban scenarios. In 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR), pages 96--101. IEEE, 2018.

[76]

Jurgen Wiest, Hendrik Deusch, Dominik Nuss, Stephan Reuter, Martin Fritzsche, and Klaus Dietmayer. Localization based on region descriptors in grid maps. In Intelligent Vehicles Symposium. IEEE, 2014.

[77]

Michael Wimmer and Claus Scheiblauer. Instant points: Fast rendering of unprocessed point clouds. In SPBG, pages 129--136. Citeseer, 2006.

[78]

Peter Wonka, Michael Wimmer, and François X Sillion. Instant visibility. In Computer Graphics Forum, volume 20, pages 411--421. Wiley Online Library, 2001.

[79]

Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015.

[80]

Wai Yeung Yan, Salem Morsy, Ahmed Shaker, and Mark Tulloch. Automatic extraction of highway light poles and towers from mobile lidar data. Optics & Laser Technology, 77:162--168, 2016.

[81]

Andi Zang, Xin Chen, and Goce Trajcevski. High definition maps in urban context. SIGSPATIAL Special, 10:15--20, 06 2018.

Digital Library

[82]

Andi Zang, Zichen Li, David Doria, and Goce Trajcevski. Accurate vehicle self-localization in high definition map dataset. In Proceedings of the 1st ACM SIGSPATIAL Workshop on High-Precision Maps and Intelligent Applications for Autonomous Vehicles. ACM, 2017.

Digital Library

[83]

Andi Zang, Runsheng Xu, Zichen Li, and David Doria. Lane boundary extraction from satellite imagery. In Proceedings of the 1st ACM SIGSPATIAL Workshop on High-Precision Maps and Intelligent Applications for Autonomous Vehicles. ACM, 2017.

Digital Library

Cited By

Zang AXu RTrajcevski GZhou F(2024)Data Issues in High-Definition Maps Furniture – A SurveyACM Transactions on Spatial Algorithms and Systems10.1145/362716010:1(1-37)Online publication date: 15-Jan-2024
https://dl.acm.org/doi/10.1145/3627160
Chaudhry NYousaf M(2023)A hash-based index for processing frequent updates and continuous location-based range queriesKnowledge and Information Systems10.1007/s10115-023-01884-965:10(4233-4271)Online publication date: 19-May-2023
https://doi.org/10.1007/s10115-023-01884-9
Teng DLiang YVo HKong JWang F(2022)Efficient 3D Spatial Queries for Complex ObjectsACM Transactions on Spatial Algorithms and Systems10.1145/35022218:2(1-26)Online publication date: 30-Jun-2022
https://dl.acm.org/doi/10.1145/3502221
Show More Cited By

Index Terms

Real-Time Applications Using High Resolution 3D Objects in High Definition Maps (Systems Paper)
1. Computer systems organization
  1. Real-time systems
    1. Real-time system specification
2. Information systems
  1. Data management systems
    1. Database design and models
      1. Relational database model
  2. Information retrieval
    1. Evaluation of retrieval results
      1. Retrieval efficiency

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SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Copyright © 2019 ACM.

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Published: 05 November 2019

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SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Zang AXu RTrajcevski GZhou F(2024)Data Issues in High-Definition Maps Furniture – A SurveyACM Transactions on Spatial Algorithms and Systems10.1145/362716010:1(1-37)Online publication date: 15-Jan-2024
https://dl.acm.org/doi/10.1145/3627160
Chaudhry NYousaf M(2023)A hash-based index for processing frequent updates and continuous location-based range queriesKnowledge and Information Systems10.1007/s10115-023-01884-965:10(4233-4271)Online publication date: 19-May-2023
https://doi.org/10.1007/s10115-023-01884-9
Teng DLiang YVo HKong JWang F(2022)Efficient 3D Spatial Queries for Complex ObjectsACM Transactions on Spatial Algorithms and Systems10.1145/35022218:2(1-26)Online publication date: 30-Jun-2022
https://dl.acm.org/doi/10.1145/3502221
Zang AZhu XLi CZhou FTrajcevski G(2022)Integrating Heterogeneous Sources for Learned Prediction of Vehicular Data Consumption2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00029(54-63)Online publication date: Jun-2022
https://doi.org/10.1109/MDM55031.2022.00029
Zang AZhu XGuo YZhou FTrajcevski G(2021)Towards Predicting Vehicular Data Consumption2021 22nd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM52706.2021.00025(109-114)Online publication date: Jun-2021
https://doi.org/10.1109/MDM52706.2021.00025
Kang YMagdy A(2020)HiDaM: A Unified Data Model for High-definition (HD) Map Data2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW49219.2020.00-11(26-32)Online publication date: Apr-2020
https://doi.org/10.1109/ICDEW49219.2020.00-11

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