IoT-Driven Collaborative Motion Estimation for Mobile Robots Using Robust ICP Techniques in 2D and 3D Environments
IEEE Internet of Things Journal, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/jiot.2026.3743791
- Dergi Adı: IEEE Internet of Things Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Compendex, INSPEC, Technology Collection (ProQuest)
- Anahtar Kelimeler: Internet of Things (IoT), Internet of Vehicles (IoV), Iterative closest point (ICP), Mapping, Mobile robots, Positioning and sensing
- İstanbul Gelişim Üniversitesi Adresli: Evet
Özet
This paper proposes a robust optimization method, Robust Iterative Closest Point (RICP), based on ICP registration for two-dimensional (2D) and three-dimensional (3D) point clouds to align corresponding points. The proposed approach is motivated by IoT-enabled robotic environments, where reliable point cloud registration is essential for mobile robot localization and navigation. Unified communication is crucial in IoT networks, which are defined by networked devices and wireless sensors. Using RICP enables efficient navigation and mapping, particularly in positioning autonomous mobile robots within IoTenabled environments. The problem of ICP-based localization is decomposed into several phases, including observation of the IoT environment, estimation of vehicle or robot motion, data matching, location updates, and registration of new landmarks. This work applies ICP to minimize relative pose errors and estimate motion. Additionally, it uses the translational vector and the rotation matrix to compute the standard deviations of the errors. Vector translation and matrix rotation are performed based on the ICP algorithm. Consequently, the translation vector and rotation matrix are updated through the singular value decomposition (SVD) method and the center of gravity (CoG) of each point cloud. Compared with conventional ICP, NDT, CPD, and other ICP-based approaches, the proposed RICP method achieves lower registration error and faster convergence. In the 3D evaluation, RICP achieved a translation error of 0.097 m, a rotation error of 0.74°, an RMSE of 0.112 m, a mismatch rate of 5.8%, and a convergence rate of 96.4%, while maintaining an average latency of 24.6 ms. The ablation study further confirms that both CoG normalization and SVD-based pose updating are necessary, as removing either component increases registration error and mismatch rate. These results demonstrate the reliability, robustness, and real-time applicability of the proposed RICP method for IoT-enabled autonomous mobile robot localization, highlighting its potential for broader industrial applications, such as warehouse automation, smart manufacturing, and autonomous delivery systems.