Bir Mikro İHA Üzerine Entegre Edilmiş Düşük Maliyetli Mesafe Sensörleri ile İki Boyutlu Labirent Haritalandırma

Öz

Bu çalışmada, düşük maliyetli VL53L1X mesafe sensörleri kullanılarak, bir mikro İHA (Crazyflie 2.0) yardımıyla iki boyutlu bir labirent ortamında haritalandırma yapılmıştır. Webots 2025a simülasyon ortamında yürütülen deneylerde, İHA'ya Multi-Ranger ve Flow Deck sensörleri entegre edilmiş, böylece çevresel algılama ve yön kontrolü sağlanmıştır. İHA, bir teleoperatör tarafından yönlendirilmiş ve dört yönden toplanan mesafe verileriyle temel bir 2D harita oluşturulmuştur. Sensör verilerindeki gürültüyü azaltmak için klasik Kalman filtresine göre daha dayanıklı olan Maximum Correntropy Kalman Filtresi (MCKF) uygulanmıştır. MCKF, Gauss dışı gürültü koşullarında yüksek doğruluk sağlayarak, sensör verilerinin güvenilirliğini artırmıştır. Sonuçlar, filtresiz verilerle oluşturulan dağınık haritaların aksine, filtreli verilerle daha net ve ayrıntılı haritalar elde edildiğini göstermiştir. Bu çalışma, düşük maliyetli ve hafif sensörlerin, uygun filtreleme yöntemleriyle birleştirildiğinde mikro İHA’larda güvenilir çevresel haritalandırma sağlayabileceğini ortaya koymuştur. Elde edilen bulgular, özellikle kapalı alanlarda kullanılacak düşük bütçeli otonom sistemlerin geliştirilmesi için önemli bir referans teşkil etmektedir.

Anahtar Kelimeler

• İnsansız Hava Araçları
• Mesafe (ToF) Sensörü
• Eşzamanlı Pozisyonlama ve Haritalandırma
• Maximum Korrentropi Kalman Filtresi (MCKF)

Two-Dimensional Maze Mapping with Low-Cost Distance Sensors Integrated on a Micro UAV

Öz

This study presents a 2D mapping approach using low-cost VL53L1X distance sensors integrated into a micro UAV (Crazyflie 2.0) in a simulated maze environment. Conducted in the Webots 2024a platform, the UAV was equipped with Multi-Ranger and Flow Deck sensors for environmental perception and control. The drone was teleoperated, and distance measurements were collected from four directions to construct a basic 2D map. To improve data reliability, the Maximum Correntropy Kalman Filter (MCKF) was applied, offering enhanced performance under non-Gaussian noise conditions compared to the classical Kalman Filter. MCKF improved state estimation accuracy and filtered out noise, resulting in more precise and detailed maps. In contrast to the noisy raw data outputs, filtered maps accurately displayed wall structures with spatial resolution up to 20 mm. The study demonstrates that when paired with effective filtering techniques, low-cost and lightweight sensors can successfully enable reliable environmental mapping in micro aerial vehicles. These findings provide a valuable foundation for the development of affordable autonomous systems, particularly for indoor applications.

Anahtar Kelimeler

• Unmanned Aerial Vehicles
• Time-of-Flight Sensor (ToF)
• Simultaneous Localization and Mapping (SLAM)
• Maximum Correntropy Kalman filter (MCKF)

Kaynakça

1. Visual-lidar odometry and mapping: low-drift, robust, and fast. Available from: https://ieeexplore.ieee.org/document/7139486 [Accessed 4 October 2025].
2. Low-drift and real-time lidar odometry and mapping | Autonomous Robots. Available from: https://link.springer.com/article/10.1007/s10514-016-9548-2 [Accessed 4 October 2025].
3. Real-time loop closure in 2D LIDAR SLAM. Available from: https://ieeexplore.ieee.org/document/7487258 [Accessed 4 October 2025].
4. Krajnik T, Majer F, Halodova L, Vintr T. Navigation without localisation: reliable teach and repeat based on the convergence theorem. arXiv 2018. doi:10.48550/arXiv.1711.05348.
5. Skorobogatov G, Barrado C, Salamí E. Multiple UAV Systems: A Survey. Unmanned Syst 2020;8(2):149-169. doi:10.1142/S2301385020500090.
6. Liu S, Li S, Zheng J, Fu Q. Benefits of using carrier phase tracking errors for ultratight GNSS/INS integration: Precise velocity estimation and fine IMU calibration. IEEE Trans Instrum Meas 2021;71:1-12.
7. Jatsun S, Emelyanova O, Leon ASM. Design of an Experimental Test Bench for a UAV Type Convertiplane. IOP Conf Ser Mater Sci Eng 2020;714(1):012009. doi:10.1088/1757-899X/714/1/012009.
8. Yanyachi DD, Anco-Agüero YH, Echaiz GA, Esquivel MA, Mamani-Saico A, Yanyachi PR. Laser_RobMap: An open source ROS2 compatible tool for 3D mapping using a Mobile Robot and 2D LiDAR. SoftwareX 2025;30:102142. doi:10.1016/j.softx.2025.102142.

9. Energy-Efficient Multi-UAV Navigation for Cooperative Data Sensing and Transmission. Available from: https://ieeexplore.ieee.org/document/11173840 [Accessed 4 October 2025].
10. Autonomous Exploration of Urban Environments using Unmanned Aerial Vehicles - Adler - 2014 - Journal of Field Robotics - Wiley Online Library. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21526 [Accessed 4 October 2025].
11. Indoor SLAM for Micro Aerial Vehicles Control Using Monocular Camera and Sensor Fusion. Available from: https://ieeexplore.ieee.org/abstract/document/7781977 [Accessed 4 October 2025].
12. Autonomous Chemical-Sensing Aerial Robot for Urban/Suburban Environmental Monitoring. Available from: https://ieeexplore.ieee.org/abstract/document/8713475 [Accessed 4 October 2025].
13. Cyberbotics: Robotics simulation with Webots. Available from: https://cyberbotics.com/ [Accessed 31 December 2023].
14. Home | Bitcraze. Available from: https://www.bitcraze.io/ [Accessed 25 May 2024].
15. Multi-ranger deck | Bitcraze. Available from: https://www.bitcraze.io/products/multi-ranger-deck/ [Accessed 12 December 2025].
16. Flow deck v2 | Bitcraze. Available from: https://www.bitcraze.io/products/flow-deck-v2/ [Accessed 14 December 2025].
17. VL53L1X | Product - STMicroelectronics. Available from: https://www.st.com/en/imaging-and-photonics-solutions/vl53l1x.html [Accessed 12 December 2025].
18. Candan F, Beke A, Kumbasar T. Design and Deployment of Fuzzy PID Controllers to the nano quadcopter Crazyflie 2.0. In: 2018 Innovations in Intelligent Systems and Applications (INISTA); 2018, p. 1-6. doi:10.1109/INISTA.2018.8466284.
19. Candan F, Dik OF, Kumbasar T, Mahfouf M, Mihaylova L. Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload. Algorithms 2023;16(6):6. doi:10.3390/a16060273.
20. Csanyi N, Toth CK. Improvement of Lidar Data Accuracy Using Lidar-Specific Ground Targets. Photogramm Eng Remote Sens 2007;73(4):385-396. doi:10.14358/PERS.73.4.385.
21. Elaksher A, Ali T, Alharthy A, Elaksher A, Ali T, Alharthy A. A Quantitative Assessment of LIDAR Data Accuracy. Remote Sens 2023;15(2). doi:10.3390/rs15020442.
22. Maximum correntropy Kalman filter. Automatica 2017;76:70-77. doi:10.1016/j.automatica.2016.10.004.
23. Candan F, Beke A, Mihaylova L. An Interacting Multiple Model Approach Based on Maximum Correntropy Student’s T Filter. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2023, p. 3314-3321. Available from: https://ieeexplore.ieee.org/abstract/document/10341366 [Accessed 17 January 2024].
24. Candan F, Li J. PCA-based Maximum Correntropy Kalman Filter Application for Agricultural Unmanned Aerial Vehicle. In: 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor); 2024, p. 689-694. doi:10.1109/MetroAgriFor63043.2024.10948842.
25. Perception Methods for Speed and Separation Monitoring Using Time-of-Flight Sensor Arrays - ProQuest. Available from: https://www.proquest.com/docview/2383056720 [Accessed 14 December 2025].
26. fethicandan/MCStF: Maximum Correntropy Student’s T Filter. Available from: https://github.com/fethicandan/MCStF [Accessed 9 December 2025].