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A LiDAR aiding ambiguity resolution method using fuzzy one-to-many feature matching

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Abstract

Despite the high-precision performance of GNSS real-time kinematic (RTK) in many cases, large noises in pseudo-range measurements or harsh signal environments still impact float ambiguity estimation in kinematic localization, which leads to ambiguity-fixed failure and worse positioning results. To improve RTK ambiguity resolution (AR) performance further, multi-sensor fusion technique is a feasible option. Light detection and ranging (LiDAR)-based localization is a good complementary method to GNSS. Tight integration of GNSS RTK and LiDAR adds new information to satellite measurements, thus improving float ambiguity estimation and then improving integer AR. In this work, a LiDAR aiding single-frequency single-epoch GPS + BDS RTK was proposed and investigated by theoretical analysis and performance assessment. Considering LiDAR-based localization failure because of ambiguous and repetitive landmarks, a fuzzy one-to-many feature-matching method was proposed to find a series of sequences including all possible relative positions to landmarks. Then, the standard RTK method was tightly combined with the possible positions from each sequence to find the most accurate position estimation. Experimental results proved the superiority of our method over the standard RTK method in all aspects of success rate, fixed rate and positioning accuracy. In specific, our method achieved centimeter-level position accuracy with 100% fixed rate in the urban environment, while the standard GPS + BDS RTK obtained decimeter-level accuracy with 26.84% fixed rate. In the high occlusion environment, our method had centimeter-level accuracy with a fixed rate of 96.31%, comparing a meter-level accuracy and a fixed rate of 7.65% of standard GPS + BDS RTK method.

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Data availability

The data that support the findings of this study are owned by Wuhan University. To access the data, please contact the author at hongjuanzhang@whu.edu.cn.

References

  • Bonnor N (2014) Principles of GNSS, inertial, and multisensor integrated navigation systems. J Navig 67:191–192. https://doi.org/10.1017/s0373463313000672

    Article  Google Scholar 

  • Deng C, Tang W, Liu J, Shi C (2014) Reliable single-epoch ambiguity resolution for short baselines using combined GPS/BeiDou system. GPS Solut 18:375–386

    Article  Google Scholar 

  • Gao Y, Liu S, Atia M, Noureldin A (2015) INS/GPS/LiDAR integrated navigation system for urban and indoor environments using hybrid scan matching algorithm. Sensors 15:23286–23302

    Article  Google Scholar 

  • Geng J, Bock Y (2013) Triple-frequency GPS precise point positioning with rapid ambiguity resolution. J Geodesy 87:449–460

    Article  Google Scholar 

  • Grejner-Brzezinska D, Da R, Toth C (1998) GPS error modeling and OTF ambiguity resolution for high-accuracy GPS/INS integrated system. J Geodesy 72:626–638

    Article  Google Scholar 

  • Groves PD (2013) Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech House, Norwood

    Google Scholar 

  • Gu S, Lou Y, Shi C, Liu J (2015) BeiDou phase bias estimation and its application in precise point positioning with triple-frequency observable. J Geodesy 89:979–992

    Article  Google Scholar 

  • Han H, Wang J, Wang J, Moraleda AH (2017) Reliable partial ambiguity resolution for single-frequency GPS/BDS and INS integration. GPS Solut 21:251–264

    Article  Google Scholar 

  • Hata A, Wolf D (2014) Road marking detection using LIDAR reflective intensity data and its application to vehicle localization. In: 17th international IEEE conference on intelligent transportation systems (ITSC), IEEE, pp 584–589

  • Hata AY, Wolf DF (2015) Feature detection for vehicle localization in urban environments using a multilayer LIDAR. IEEE Trans Intell Transp Syst 17:420–429

    Article  Google Scholar 

  • He H, Li J, Yang Y, Xu J, Guo H, Wang A (2014) Performance assessment of single-and dual-frequency BeiDou/GPS single-epoch kinematic positioning. GPS Solut 18:393–403

    Article  Google Scholar 

  • Joerger M, Pervan B (2009) Measurement-level integration of carrier-phase GPS and laser-scanner for outdoor ground vehicle navigation. J Dyn Syst Meas Control 131:021004

    Article  Google Scholar 

  • Levinson J, Thrun S (2010) Robust vehicle localization in urban environments using probabilistic maps. In: 2010 IEEE international conference on robotics and automation, IEEE, pp 4372–4378

  • Levinson J, Montemerlo M, Thrun S (2007) Map-based precision vehicle localization in urban environments. In: Robotics: science and systems, Citeseer, p 1

  • Li B, Feng Y, Shen Y (2010) Three carrier ambiguity resolution: distance-independent performance demonstrated using semi-generated triple frequency GPS signals. GPS Solut 14:177–184

    Article  Google Scholar 

  • Li X, Ge M, Dai X, Ren X, Fritsche M, Wickert J, Schuh H (2015) Accuracy and reliability of multi-GNSS real-time precise positioning: GPS, GLONASS, BeiDou, and Galileo. J Geodesy 89:607–635

    Article  Google Scholar 

  • Liu R, Wang J, Zhang B (2020) High definition map for automated driving: overview and analysis. J Navig 73:324–341

    Article  Google Scholar 

  • Lu W, Zhou Y, Wan G, Hou S, Song S (2019) L3-Net: towards learning based LiDAR localization for autonomous driving. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6389–6398

  • Odolinski R, Teunissen PJ, Odijk D (2015) Combined BDS, Galileo, QZSS and GPS single-frequency RTK. GPS Solut 19:151–163

    Article  Google Scholar 

  • Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66

    Article  Google Scholar 

  • Puente I, González-Jorge H, Martínez-Sánchez J, Arias P (2013) Review of mobile mapping and surveying technologies. Measurement 46:2127–2145

    Article  Google Scholar 

  • Refan MH, Dameshghi A, Kamarzarrin M (2014) Improving RTDGPS accuracy using hybrid PSOSVM prediction model. Aerosp Sci Technol 37:55–69

    Article  Google Scholar 

  • Saltzmann M (1993) Least squares filtering and testing for geodetic navigation applications. Publications on geodesy, p 37

  • Scherzinger BM (2000) Precise robust positioning with inertial/GPS RTK. In: Proceedings of the 13th international technical meeting for the satellite division of the institute of navigation (ION GPS), pp 115–162

  • Soloviev A (2008) Tight coupling of GPS, laser scanner, and inertial measurements for navigation in urban environments. In: 2008 IEEE/ION position, location and navigation symposium, IEEE, pp 511–525

  • Tang W, Deng C, Shi C, Liu J (2014) Triple-frequency carrier ambiguity resolution for Beidou navigation satellite system. GPS Solut 18:335–344

    Article  Google Scholar 

  • Tang J et al (2015) LiDAR scan matching aided inertial navigation system in GNSS-denied environments. Sensors 15:16710–16728

    Article  Google Scholar 

  • Tao Q, Hu Z, Cai H, Huang G, Wu J (2018) Coding pavement lanes for accurate self-localization of intelligent vehicles. In: 2018 IEEE intelligent vehicles symposium (IV), IEEE, pp 1458–1463

  • Teunissen PJ (1999) An optimality property of the integer least-squares estimator. J Geodesy 73:587–593

    Article  Google Scholar 

  • Teunissen P, De Jonge P, Tiberius C (1995) The LAMBDA method for fast GPS surveying. In: International symposium “GPS technology applications”, Bucharest, Romania

  • Teunissen P, Odolinski R, Odijk D (2014) Instantaneous BeiDou + GPS RTK positioning with high cut-off elevation angles. J Geodesy 88:335–350

    Article  Google Scholar 

  • Wan G, Yang X, Cai R, Li H, Zhou Y, Wang H, Song S (2018) Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes. In: 2018 IEEE international conference on robotics and automation (ICRA), IEEE, pp 4670–4677

  • Wang J (1999) Stochastic modeling for real-time kinematic GPS/GLONASS positioning. Navigation 46:297–305

    Article  Google Scholar 

  • Yinglei X, Qunzhan L, Shaofeng X, Liyan Z (2009) Study on algorithm and communication protocol of differential GPS positioning based on pseudorange. In: 2009 International forum on information technology and applications, IEEE, pp 606–609

  • Zhang X, He X (2016) Performance analysis of triple-frequency ambiguity resolution with BeiDou observations. GPS Solut 20:269–281

    Article  Google Scholar 

  • Zhang X, Zhu F, Zhang Y, Mohamed F, Zhou W (2019) The improvement in integer ambiguity resolution with INS aiding for kinematic precise point positioning. J Geodesy 93:993–1010

    Article  Google Scholar 

  • Zheng S, Wang J (2017) High definition map-based vehicle localization for highly automated driving: geometric analysis. In: 2017 international conference on localization and GNSS (ICL-GNSS), IEEE, pp 1–8

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Acknowledgements

This research was funded by the National Key Research and Development Program (No. 2018YFB1600600), the National Natural Science Foundation of China (Project No. 41801377, Project No. U1764262) and the Fundamental Research Funds for the Central Universities (No. 20422019KF0034).

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Authors and Affiliations

  1. GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China

    Chuang Qian, Bao Shu, Jian Tang & Hui Liu

  2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China

    Hongjuan Zhang, Wenzhuo Li & Bijun Li

  3. Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China

    Hongjuan Zhang & Bijun Li

  4. School of Computer Science and Technology, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, China

    Zhijun Chen

Authors
  1. Chuang Qian
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  2. Hongjuan Zhang
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  3. Wenzhuo Li
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  4. Bao Shu
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  5. Jian Tang
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  6. Bijun Li
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  7. Zhijun Chen
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  8. Hui Liu
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Contributions

CQ, HZ and WL designed and performed the experiments, analyzed the data in the paper and wrote the paper; BS, JT and ZC helped to analyze the data and write the paper; JT provided the hardware platform and helped perform the experiments; and BL and HL conceived the framework of this research.

Corresponding author

Correspondence to Hongjuan Zhang.

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Qian, C., Zhang, H., Li, W. et al. A LiDAR aiding ambiguity resolution method using fuzzy one-to-many feature matching. J Geod 94, 98 (2020). https://doi.org/10.1007/s00190-020-01426-z

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  • DOI: https://doi.org/10.1007/s00190-020-01426-z

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