Real-Time Pose Estimation for Autonomous Vehicles Using Probabilistic Landmark Maps and Sensor Fusion
(1) * Wael A. Farag Mail (Cairo University, Egypt)
(2) Mohamed Fayed Mail (American University of the Middle East, Kuwait)
*corresponding author

Abstract


This study introduces a robust and accurate method for estimating autonomous vehicle position, facilitating safe navigation in urban and highway settings. The proposed technique employs a probabilistic particle filter framework, which, unlike approaches constrained by Gaussian assumptions, represents probability densities as samples, enabling more flexible position estimation. A key innovation lies in integrating a finely tuned Unscented Kalman Filter (UKF) to fuse radar and lidar data specifically for robust detection of pole-like static landmarks, whose positions and associated uncertainties are probabilistically modeled within an offline reference map. The particle filter leverages Bayesian filtering, associating UKF-derived landmark observations with this probabilistic map to refine the vehicle's pose. Broad simulation tests validate the method's effectiveness, achieving a mean localization error of approximately 11 cm in both longitudinal and lateral directions. Furthermore, the system demonstrates robustness, maintaining localization accuracy below 30 cm even with landmark position uncertainties up to 2 meters, and confirms real-time capability exceeding 100 Hz. These findings establish the approach as a reliable and precise solution for autonomous vehicle localization across various scenarios.


Keywords


UKF; Localization; ADAS; Monte Carlo; Autonomous Driving; Particle Filter; Sensor Fusion; Kalman Filter

   

DOI

https://doi.org/10.31763/ijrcs.v5i2.1851 		      

Article metrics

10.31763/ijrcs.v5i2.1851 Abstract views : 1 | PDF views : 2

   

Cite

How to cite item
   

Full Text

Download

References


[1] E. Yurtsever, J. Lambert, A. Carballo and K. Takeda, “A Survey of Autonomous Driving: Common Practices and Emerging Technologies,” IEEE Access, vol. 8, pp. 58443-58469, 2020, https://doi.org/10.1109/ACCESS.2020.2983149.

[2] W. Farag, “A lightweight vehicle detection and tracking technique for advanced driving assistance systems,” Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2693–2710, 2020, https://doi.org/10.3233/JIFS-190634.

[3] W. Farag and Z. Saleh, “An advanced vehicle detection and tracking scheme for self-driving cars,” 2nd Smart Cities Symposium (SCS 2019), pp. 1-6, 2019, https://doi.org/10.1049/cp.2019.0222.

[4] W. Farag, “Multiple Road-Objects Detection and Tracking for Autonomous Driving,” Journal of Engineering Research, vol. 10, no. 1A, pp. 237-262, 2021, https://doi.org/10.36909/jer.10993.

[5] W. Farag, “Lidar and radar fusion for real-time road-objects detection and tracking,” Intelligent Decision Technologies, vol. 15, no. 2, pp. 291-304, 2021, https://doi.org/10.3233/IDT-200106.

[6] W. Farag, “A Comprehensive Real-Time Road-Lanes Tracking Technique for Autonomous Driving,” International Journal of Computing and Digital Systems, vol. 9, no. 3, pp. 349-362, 2020, https://doi.org/10.12785/ijcds/090302.

[7] W. Farag, “Real-Time Detection of Road Lane-Lines for Autonomous Driving,” Recent Advances in Computer Science and Communications, vol. 13, no. 2, pp. 265-274, 2020, http://dx.doi.org/10.2174/2213275912666190126095547.

[8] W. Farag, “Traffic signs classification by deep learning for advanced driving assistance systems,” Intelligent Decision Technologies, vol. 13, no. 3, pp. 305-314, 2019, https://doi.org/10.3233/IDT-180064.

[9] D. C. Guastella and G. Muscato, “Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review,” Sensors, vol. 21, no. 1, p. 73, 2020, https://doi.org/10.3390/s21010073.

[10] S.-J. Babak, S. A. Hussain, B. Karakas, and S. Cetin, “Control of autonomous ground vehicles: a brief technical review,” IOP Conference Series: Materials Science and Engineering, vol. 224, no. 1, p. 012029, 2017, https://doi.org/10.1088/1757-899X/224/1/012029.

[11] W. Farag and Z. Saleh, “Traffic signs identification by deep learning for autonomous driving,” Smart Cities Symposium 2018, pp. 1-6, 2018, https://doi.org/10.1049/cp.2018.1382.

[12] W. A. Farag and W. H. F. Aly, “Computer Vision-Based Road Vehicle Tracking For Self-Driving Car Systems,” Journal of Southwest Jiaotong University, vol. 58, no. 3, pp. 785-802, 2023, https://doi.org/10.35741/issn.0258-2724.58.3.66.

[13] W. Farag, “Road-objects tracking for autonomous driving using lidar and radar fusion,” Journal of Electrical Engineering, vol. 71, no. 3, pp. 138–149, 2020, https://sciendo.com/article/10.2478/jee-2020-0021.

[14] A. Woo, B. Fidan, and W. W. Melek, “Localization for Autonomous Driving,” Handbook of Position Location: Theory, Practice, and Advances, Second Edition, Second Edition, 2019, https://doi.org/10.1002/9781119434610.ch29.

[15] R. Zekavat and R. M. Buehrer, “Localization for Autonomous Driving,” Handbook of Position Location, pp. 1051-1087, 2018, https://doi.org/10.1002/9781119434610.ch29.

[16] W. Farag, “Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles,” Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, vol. 235, no. 7, pp. 1125-1138, 2021, https://doi.org/10.1177/0959651820975523.

[17] H. Ma, W. Pei, and Q. Zhang, “Research on Path Planning Algorithm for Driverless Vehicles,” Mathematics, vol. 10, no. 15, p. 2555, 2022, https://doi.org/10.3390/math10152555.

[18] W. Farag, “Recognition of traffic signs by convolutional neural nets for self-driving vehicles,” International Journal of Knowledge-Based and Intelligent Engineering Systems, vol. 22, no. 3, pp. 205-214, 2018, https://doi.org/10.3233/KES-180385.

[19] W. Farag, M. Abouelela, and M. Helal, “Finding and Tracking Automobiles on Roads for Self-Driving Car Systems,” International Journal of Robotics and Control Systems, vol. 3, no. 4, pp. 704-727, 2023, https://doi.org/10.31763/ijrcs.v3i4.1022.

[20] S. Kuutti, S. Fallah, K. Katsaros, M. Dianati, F. Mccullough and A. Mouzakitis, “A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 829-846, 2018, https://doi.org/10.1109/JIOT.2018.2812300.

[21] W. Farag, “Complex track maneuvering using real-time MPC control for autonomous driving,” International Journal of Computing and Digital Systems, vol. 90, no. 5, 2020, https://doi.org/10.12785/ijcds/090511.

[22] W. Farag, “Complex-Track Following in Real-Time Using Model-Based Predictive Control,” International Journal of Intelligent Transportation Systems Research, vol. 19, pp. 112-127, 2021, https://doi.org/10.1007/s13177-020-00226-1.

[23] W. Farag, “Track maneuvering using PID control for self-driving cars,” Recent Advances in Electrical and Electronic Engineering, vol. 13, no. 1, pp. 91-100, 2020, http://dx.doi.org/10.2174/2352096512666190118161122.

[24] W. Farag and Z. Saleh, “Tuning of PID Track Followers for Autonomous Driving,” 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 1-7, 2018, https://doi.org/10.1109/3ICT.2018.8855773.

[25] W. Farag and Z. Saleh, “Road Lane-Lines Detection in Real-Time for Advanced Driving Assistance Systems,” 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 1-8, 2018, https://doi.org/10.1109/3ICT.2018.8855797.

[26] Y. Liu, X. Pei, X. Guo, C. Chen, and H. Zhou, “An integration planning and control method of intelligent vehicles based on the iterative linear quadratic regulator,” Journal of the Franklin Institute, vol. 361, no. 1, pp. 265-282, 2024, https://doi.org/10.1016/j.jfranklin.2023.11.046.

[27] W. Farag, “Multi-Agent Reinforcement Learning using the Deep Distributed Distributional Deterministic Policy Gradients Algorithm,” 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT), pp. 1-6, 2020, https://doi.org/10.1109/3ICT51146.2020.9311945.

[28] A. Schaefer, D. Büscher, J. Vertens, L. Luft and W. Burgard, “Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans,” 2019 European Conference on Mobile Robots (ECMR), pp. 1-7, 2019, https://doi.org/10.1109/ECMR.2019.8870928.

[29] J. Levinson, M. Montemerlo, and S. Thrun, “Map-Based Precision Vehicle Localization in Urban Environments,” Robotics: Science and Systems III, 2024, https://www.roboticsproceedings.org/rss03/p16.pdf.

[30] F. Lu, G. Chen, J. Dong, X. Yuan, S. Gu and A. Knoll, “Pole-based Localization for Autonomous Vehicles in Urban Scenarios Using Local Grid Map-based Method,” 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 640-645, 2020, https://doi.org/10.1109/ICARM49381.2020.9195330.

[31] L. de Paula Veronese et al., “Evaluating the Limits of a LiDAR for an Autonomous Driving Localization,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1449-1458, 2021, https://doi.org/10.1109/TITS.2020.2971054.

[32] T. Zhou, M. Yang, K. Jiang, H. Wong, and D. Yang, “MMW Radar-Based Technologies in Autonomous Driving: A Review,” Sensors, vol. 20, no. 24, p. 7283, 2020, https://doi.org/10.3390/s20247283.

[33] A. Takanose, Y. Atsumi, K. Takikawa, and J. Meguro, “Improvement of Reliability Determination Performance of Real-Time Kinematic Solutions Using Height Trajectory,” Sensors, vol. 21, no. 2, p. 657, 2021, https://doi.org/10.3390/s21020657.

[34] S. S. Rathour, A. Boyali, L. Zheming, Seiichi. Mita, and V. John, “A Map-based Lateral and Longitudinal DGPS/DR Bias Estimation Method for Autonomous Driving,” International Journal of Machine Learning and Computing, vol. 7, no. 4, pp. 67-71, 2017, https://www.ijml.org/vol7/622-S78.pdf.

[35] N. Carlevaris-Bianco, A. Ushani, and R. M. Eustice, “NCLT Dataset: The University of Michigan North Campus Long-Term Vision and LIDAR Dataset,” The University of Michigan, 2016, https://robots.engin.umich.edu/nclt/.

[36] M. Modsching, R. Kramer, and K. Hagen, “Field trial on GPS accuracy in a medium-size city: the influence of built-up,” 3rd Workshop on Positioning, Navigation, and Communication, pp. 209-218, 2006, http://www.modsching.com/papers/FieldtrialonGPSAccuracy10pages2006-02-10_06crcit.pdf.

[37] A. Rakhmanov and Y. Wiseman, “Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles,” Remote Sensing, vol. 15, no. 8, p. 2165, 2023, https://doi.org/10.3390/rs15082165.

[38] W. Li, Z. Li, W. Jiang, Q. Chen, G. Zhu, and J. Wang, “A New Spatial Filtering Algorithm for Noisy and Missing GNSS Position Time Series Using Weighted Expectation Maximization Principal Component Analysis: A Case Study for Regional GNSS Network in Xinjiang Province,” Remote Sensing, vol. 14, no. 5, p. 1295, 2022, https://doi.org/10.3390/rs14051295.

[39] W. Farag, “Robot arm navigation using deep deterministic policy gradient algorithms,” Journal of Experimental and Theoretical Artificial Intelligence, vol. 35, no. 5, pp. 617-627, 2023, https://doi.org/10.1080/0952813X.2021.1960640.

[40] W. Farag, “Real-time lidar and radar fusion for road-objects detection and tracking,” International Journal of Computational Science and Engineering, vol. 24, no. 5, p. 517, 2021, https://doi.org/10.1504/IJCSE.2021.118100.

[41] J. Levinson and S. Thrun, “Robust vehicle localization in urban environments using probabilistic maps,” 2010 IEEE International Conference on Robotics and Automation, pp. 4372-4378, 2010, https://doi.org/10.1109/ROBOT.2010.5509700.

[42] A. A. Soltani, H. Huang, J. Wu, T. D. Kulkarni and J. B. Tenenbaum, “Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2511-2519, 2017, https://doi.org/10.1109/CVPR.2017.269.

[43] X. Qin, Y. Luo, N. Tang, G. Li, “Making data visualization more efficient and effective: a survey,” The VLDB Journal, vol. 29, no. 1, pp. 93-117, 2020, https://doi.org/10.1007/s00778-019-00588-3.

[44] William E. Lorensen, Harvey E. Cline, “Marching cubes: A high-resolution 3D surface construction algorithm,” ACM SIGGRAPH Computer Graphics, vol. 21, no. 4, pp. 163-169, 1987, https://doi.org/10.1145/37402.37422.

[45] K. Wong, Y. Gu and S. Kamijo, “Mapping for Autonomous Driving: Opportunities and Challenges,” IEEE Intelligent Transportation Systems Magazine, vol. 13, no. 1, pp. 91-106, 2021, https://doi.org/10.1109/MITS.2020.3014152.

[46] J. Kümmerle, M. Sons, F. Poggenhans, T. Kühner, M. Lauer and C. Stiller, “Accurate and Efficient Self-Localization on Roads using Basic Geometric Primitives,” 2019 International Conference on Robotics and Automation (ICRA), pp. 5965-5971, 2019, https://doi.org/10.1109/ICRA.2019.8793497.

[47] L. Weng, M. Yang, L. Guo, B. Wang and C. Wang, “Pole-Based Real-Time Localization for Autonomous Driving in Congested Urban Scenarios,” 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 96-101, 2018, https://doi.org/10.1109/RCAR.2018.8621688.

[48] M. Fischler and R. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981, https://doi.org/10.1145/358669.358692.

[49] M. Sefati, M. Daum, B. Sondermann, K. D. Kreisköther and A. Kampker, “Improving vehicle localization using semantic and pole-like landmarks,” 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 13-19, 2017, https://doi.org/10.1109/IVS.2017.7995692.

[50] W. Farag, “Navigation of Robotic-Arms using Policy Gradient Reinforcement Learning,” International Journal of Computing and Digital Systems, vol. 12, no. 1, 2022, https://doi.org/10.12785/ijcds/120171.

[51] A. Schaefer, D. Büscher, J. Vertens, L. Luft, and W. Burgard, “Long-term vehicle localization in urban environments based on pole landmarks extracted from 3-D lidar scans,” Robotics and Autonomous Systems, vol. 136, p. 103709, 2021, https://doi.org/10.1016/j.robot.2020.103709.

[52] K. Chiang, Y. Chiu, S. Srinara, and M. Tsai, “Performance of LiDAR-SLAM-based PNT with initial poses based on NDT scan matching algorithm,” Satellite Navigation, vol. 4, no. 1, p. 3, 2023, https://doi.org/10.1186/s43020-022-00092-0.

[53] H. Taheri and Z. C. Xia, “SLAM; definition and evolution,” Engineering Applications of Artificial Intelligence, vol. 97, p. 104032, 2021, https://doi.org/10.1016/j.engappai.2020.104032.

[54] J. K. Suhr, J. Jang, D. Min and H. G. Jung, “Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 5, pp. 1078-1086, 2017, https://doi.org/10.1109/TITS.2016.2595618.

[55] F. Lu and E. Milios, “Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans,” Journal of Intelligent and Robotic Systems, vol. 18, no. 3, pp. 249-275, 1997, https://doi.org/10.1023/A:1007957421070.

[56] S. Thrun, “Particle Filters in Robotics,” Proceedings of Uncertainty in AI (UAI) 2002, 2002, https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=e6604ff9a3cceb37329096df80eb9cec54a385b9.

[57] B. Chen, L. Dang, N. Zheng, J. C. Principe, “Kalman Filtering Under Information Theoretic Criteria,” Kalman Filtering Under Information Theoretic Criteria, pp. 89-126, 2023, https://doi.org/10.1007/978-3-031-33764-2_4.

[58] E. A. Wan and R. Van Der Merwe, “The unscented Kalman filter for nonlinear estimation,” Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373), pp. 153-158, 2000, https://doi.org/10.1109/ASSPCC.2000.882463.

[59] G. A. Einicke and L. B. White, “Robust extended Kalman filtering,” IEEE Transactions on Signal Processing, vol. 47, no. 9, pp. 2596-2599, 1999, https://doi.org/10.1109/78.782219.

[60] W. Farag, “Synthesis of intelligent hybrid systems for modeling and control,” University of Waterloo, 1998, https://uwspace.uwaterloo.ca/bitstream/handle/10012/238/NQ30606.pdf;sequence=1.

[61] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” KDD’96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226-231, 1996, http://file.biolab.si/papers/1996-DBSCAN-KDD.pdf.

[62] D. Kellner, J. Klappstein and K. Dietmayer, “Grid-based DBSCAN for clustering extended objects in radar data,” 2012 IEEE Intelligent Vehicles Symposium, pp. 365-370, 2012, https://doi.org/10.1109/IVS.2012.6232167.

[63] R. A. Brown, “Building a Balanced k-d Tree in O(kn log n) Time,” Journal of Computer Graphics Techniques (JCGT), vol. 4, no. 1, pp. 50-68, 2015, http://jcgt.org/published/0004/01/03/.

[64] W. A. Farag, J. M. H. Barakat, “Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization,” World Electric Vehicle Journal, vol. 15, no. 1, p. 5, 2024, https://doi.org/10.3390/wevj15010005.

[65] M. Nagiub and W. Farag, “Automatic selection of compiler options using genetic techniques for embedded software design,” 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 69-74, 2013, https://doi.org/10.1109/CINTI.2013.6705166.

[66] K. H. A. Faraj, K. H. Ahmed, T. N. A. A. Attar, W. M. Hameed, A. B. Kanbar, “Response time analysis for XAMPP server based on different versions of linux operating system,” The Scientific Journal of Cihan University–Sulaimaniya, vol. 4, no. 2, pp. 102-114, 2020, https://doi.org/10.25098/4.2.23.

[67] A. Chalvatzaras, I. Pratikakis and A. A. Amanatiadis, “A Survey on Map-Based Localization Techniques for Autonomous Vehicles,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1574-1596, 2023, https://doi.org/10.1109/TIV.2022.3192102.

[68] S. Zhao and B. Huang, “On initialization of the Kalman filter,” 2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP), pp. 565-570, 2017, https://doi.org/10.1109/ADCONIP.2017.7983842.

[69] R. Piché, “Online tests of Kalman filter consistency,” International Journal of Adaptive Control and Signal Processing, vol. 30, no. 1, pp. 115-124, 2016, https://doi.org/10.1002/acs.2571.

[70] D. R. Niranjan, B. C. VinayKarthik, “Deep learning based object detection model for autonomous driving research using carla simulator,” 2021 2nd international conference on smart electronics and communication (ICOSEC), pp. 1251-1258, 2021, https://doi.org/10.1109/ICOSEC51865.2021.9591747.

[71] W. Farag, “Real-Time Autonomous Vehicle Localization Based on Particle and Unscented Kalman Filters,” Journal of Control, Automation and Electrical Systems, vol. 32, no. 2, pp. 309-325, 2021, https://doi.org/10.1007/s40313-020-00666-w.

[72] R. Spangenberg, D. Goehring and R. Rojas, “Pole-based localization for autonomous vehicles in urban scenarios,” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2161-2166, 2016, https://doi.org/10.1109/IROS.2016.7759339.

[73] H. Gao, B. Cheng, J. Wang, K. Li, J. Zhao and D. Li, “Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment,” IEEE Transactions on Industrial Informatics, vol. 14, no. 9, pp. 4224-4231, 2018, https://doi.org/10.1109/TII.2018.2822828.

[74] H. Gao et al., “Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections,” Science China Information Sciences, vol. 64, no. 7, p. 172207, 2021, https://doi.org/10.1007/s11432-020-3071-8.

[75] W. A. Farag, V. H. Quintana and G. Lambert-Torres, “Neuro-fuzzy modeling of complex systems using genetic algorithms,” Proceedings of International Conference on Neural Networks (ICNN'97), vol. 1, pp. 444-449, 1997, https://doi.org/10.1109/ICNN.1997.611709.

[76] W. Farag, “Real-time NMPC path tracker for autonomous vehicles,” Asian Journal of Control, vol. 23, no. 4, pp. 1952-1965, 2021, https://doi.org/10.1002/asjc.2335.

[77] W. A. Farag, M. Fayed, “Advancing vehicle detection for autonomous driving: integrating computer vision and machine learning techniques for real-world deployment,” Journal of Control and Decision, Taylor & Francis, 2025, https://doi.org/10.1080/23307706.2025.2469893.

[78] W. A. Farag, M. Helal, “Real-time localization with probabilistic maps and unscented kalman filtering: a dynamic sensor fusion approach,” Journal of Control and Decision, 2024, https://doi.org/10.1080/23307706.2024.2417218.

[79] Y. Kawai, J. Padron, Y. Yokokura, K. Ohishi and T. Miyazaki, “Equivalent Disturbance Compensator and Friction Compensation for Back-Forward Drivability Improvement,” IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, pp. 1-6, 2021, https://doi.org/10.1109/IECON48115.2021.9589468.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Wael A Farag

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

About the JournalJournal PoliciesAuthor Information

International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia