Reducing GPS Impreciseness by Odometer Sensor Reading to Improve Positioning Accuracy

Year 2021, Volume: 5 Issue: 4, 299 - 307, 31.12.2021

Murat Bakırcı

https://doi.org/10.30939/ijastech..977039

Abstract

Positioning accuracy is becoming more and more important as autonomous vehi-cle technology develops. The focus of this paper is on an onboard smart mobile de-vice’s feature to improve positioning accuracy, based on experimentally acquired GPS and odometer sensor data. A simplificative odometer reading based approach is applied instead of using more advanced smart device sensors (e.g., accelerometer, gy-roscope). Numerous driving tests were performed and analyzed to collect sufficient dynamic travel data. Traveled distances between two consecutive positions from GPS data are computed and correlated with a vehicle’s speed profile between the same two positions. To calculate distance more precisely speed values from GPS are corrected with odometer sensor reading. The results revealed an average increase in accuracy of 20%. The developed model can be incorporated with a smart device’s other low-energy sensors. Using the smart device sensors, the developed model can be extended to acquire a more accurate positioning.

Keywords

GPS Inaccuracy, Smart Mobile Device, Odometer, Dynamic Travel Data, Intelligent Transportation Systems, positioning

References

• [1] Lee JY, Kim HS, Choi KH, Lim J, Chun S, Lee HK. Adaptive GPS/INS integration for relative navigation. GPS Solutions. 2016;20(1):63-75.
• [2] Xu Q, Li X, Chan CY. Enhancing localization accuracy of MEMS-INS/GPS/In-vehicle sensors integration during GPS outages. IEEE Transactions of Instrumentation and Measurement. 2018;67(8):1966-1978.
• [3] Gakne PV, O’Keefe K. Tightly-coupled GNSS/vision using a sky-pointing camera for vehicle navigation in urban areas. Sensors. 2018;18(4):1244.
• [4] Cai H, Hu Z, Huang G, Zhu D, Su X. Integration of GPS, monoc-ular vision, and high definition (HD) map for accurate vehicle lo-calization. Sensors. 2018;18(10):3270.
• [5] Drawil NM, Amar HM, Basir OA. GPS localization accuracy classification: a context-based approach. IEEE Transactions on In-telligent Transportation Systems. 2013;14(1):262–273.
• [6] Meng X, Wang H, Liu B. A robust vehicle localization approach based on GNSS/IMU/ DMI/LiDAR sensor fusion for autonomous vehicles. Sensors. 2017;17(9):2140.
• [7] Marais J, Ambellouis S, Flancquart A, Lefebvre S, Meurie C, Ruichek Y. Accurate localization based on GNSS and propagation knowledge for safe applications in guided transport. Social and Be-havioral Sciences. 2012;48:796–805.
• [8] Li X, Guo J, Zhang D. An algorithm of GPS single-epoch kine-matic positioning based on doppler velocimetry. Geomatics and In-formation Science of Wuhan University. 2018;43:1036-1041.
• [9] Ge Y, Dai P, Qin W, Yang X, Zhou F, Wang S, Zhao X. Perfor-mance of multi-GNSS precise point positioning time and frequency transfer with clock modeling. Remote Sensing. 2019;11:347.
• [10] Yu X, Gao J. Kinematic precise point positioning using multi-constellation global navigation satellite system (GNSS) observa-tions. ISPRS International Journal of Geo-Information. 2017;6(6):1-15.
• [11] Cristorado C, Falco G, Ruotsalainen L, Dovis F. On the use of an ultra-tight integration for robust navigation in jammed scenarios. 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation, Miami, Florida, USA, 2019.
• [12] Jiang H, Hu J, An S, Wang M, Park BB. Eco approaching at an isolated signalized intersection under partially connected and auto-mated vehicles environment. Transportation Research Part C: Emerging Technologies. 2017;79:290–307.
• [13] Patel RH, Harri J, Bonnet C. Impact of localization errors on auto-mated vehicle control strategies. IEEE Vehicular Networking Con-ference. Italy, 2017;61–68.
• [14] Li Y, Hu ZZ, Hu YZ, Chu D. Integration of vision and topological self-localization for intelligent vehicles. Mechatronics. 2018;51:46-58.
• [15] Marais J, Meurie C, Attia D, Ruichek Y, Flancquart A. Toward accurate localization in guided transport: combining GNSS data and imaging information. Transportation Research Part C: Emerging Technologies. 2013;<hal-00879677>.
• [16] Sekaran J, Kaluvan H, Irudhayaraj L. Modeling and analysis of GPS-GLONASS navigation for car like mobile robot. Journal of Electrical Engineering Technology. 2020;15:927-935.
• [17] Hsu L, Gu Y, Kamijo S. 3D building model-based pedestrian posi-tioning method using GPS/ GLONASS/QZSS and its reliability calculation. GPS Solutions. 2016;20:413-428.
• [18] Ioannides RT, Strangeways HJ. Improved ionospheric correction for dual frequency GPS. Department of Electronics and Electrical Engineering, The University of Leeds LS2 9JT, UK.
• [19] Keicher R, Seufert H. Automatic guidance for agricultural vehicles in Europe. Computers and Electronics in Agriculture. 2000;25:169–194.
• [20] Karaim M. Ultra-tight GPS/INS integrated systems for land vehicle navigation in challenging environments. Dissertation. Queen’s University, 2019.
• [21] Krach B, Lentmaier M, Robertson P. Joint Bayesian positioning and multipath migitation in GNSS. IEEE. 2008;DOI:10.1109/ICASSP.2008.4518390:3437–3440.
• [22] Ng Y, Gao GX. GNSS multireceiver vector tracking. IEEE Trans-actions on Aerospace and Electronic Systems. 2017;53:2583-2593.
• [23] Brida P, Machaj J, Benikovsky J. A modular localization system as a positioning service for road transport. Sensors. 2014;14:20274–20296.
• [24] Schmidt GT. GPS based navigation systems in difficult environ-ments. Gyroscopy and Navigation. 2019;10:41-53.
• [25] Fan J, Ma G. Characteristics of GPS positioning error with non-uniform pseudorange error. GPS Solutions. 2014;18(4):615–623.
• [26] Dautermann T, Mayer C, Antreich F, Konovaltsev A, Belabbas B, Kalberer U. Non-Gausian error modeling for GBAS integrity assesment. IEEE Transactions on Aerospace and Electronics Sys-tems. 2012;48(1):1-14.
• [27] Lin CE, Li CC, Yang SH, Lin SH, Lin CY. Development of on-line diagnostic and real time early warning system for vehicles. Sensors for Industry Conference. Houston, Texas, USA, 2005.
• [28] Li R, Wang S, Long Z, Gu D. Undeepvo: monocular visual odometry through unsupervised deep learning. IEEE International Conference on Robotics and Automation. Australia, 2018;7286-7291.
• [29] Berger M, Platzer M. Field evaluation of the smartphone-based travel behaviour data collection app “SmartMo”. 10th International Conference on Transport Survey Methods, Transportation Re-search Procedia. 2015;11:263–279.
• [30] Castrogiovanni P, Fadda E, Perboli G, Rizzo A. Smartphone data classification technique for detecting the usage of public or private transportation modes. IEEE Access. 2020;8:58377-58391.
• [31] Mukheja P, Velaga NR, Sharmila RB. Smartphone-based crowdsourcing for position estimation of public transport vehicles. IET Intelligent Transport Systems. 2017;11(9):588-595.
• [32] Yang F, Chen L, Cheng Y, Luo X, Ran B. An empirical study of parameter estimation for stated preference experimental design. Mathematical Problems in Engineering. 2014;2014: ID 292608, http://dx.doi.org/10.1155 /2014/292608.
• [33] Talebpour A, Mahmassani HS, Bustamante FE. Modeling driver behavior in a connected environment: integrated microscopic simu-lation of traffic and mobile wireless telecommunication systems. Transportation Research Record. 2016;2560(1):75-86.
• [34] Ma X, Wu YJ, Wang Y. E-science tansportation platform for data sharing, visualization, modeling, and analysis. Transportation Re-search Record: Journal of the Transporttion Research Board. 2011;2215:37–49.
• [35] Karlaftis MG, Vlahogianni EI. Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transportation Research Part C. 2011;19:387–399.
• [36] Piasco N, Sidibe D, Demonceaux C, Gouet-Brunet V. A survey on visual based localization: on the benefit of heterogeneous data. Pat-tern Analysis and Applications. 2018;74:90-109.
• [37] Antoniou C, Koutsopoulos HN. Estimation of traffic dynamics models with machine-learning methods. Transportation Research Record: Journal of the Transporttion Research Board. 2006;1965:103–111.