Aug 2026· 2026 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM)· pp. 451-455· 0 citations· 4 references
Abstract
This study proposes a multi-sensor fusion-based Simultaneous Localization and Mapping framework (LIOG-SLAM) to address positioning errors and drift issues encountered by substation inspection robots in large-scale substations. By integrating 3D LiDAR, Inertial Measurement Unit (IMU), Wheel Odometry (ODO), and Global Navigation Satellite System (GNSS) data, combined with ground-optimized odometry constraints, IMU-ODO joint pre-integration, sliding window marginalization, and loop closure detection, positioning accuracy and system robustness have been enhanced. In the KITTI dataset and Gazebo simulation environment, LIOG-SLAM demonstrated higher accuracy and lower error compared to traditional algorithms such as A-LOAM and LIO-SAM. Specifically, the root mean square error of APE decreased by 9.1 m and 5.207 m on the same sequence dataset, while maintaining efficient real-time performance in complex environments. This method offers a novel solution for efficient inspection in substations.
With the rapid development of industrial automation and intelligent logistics, the autonomous navigation and localization accuracy of automated guided vehicles (AGVs), as core equipment in automated transport systems, directly affects logistics efficiency and operational safety. To address insufficient AGV localization...
In robotic operation scenarios, LiDAR-Inertial SLAM systems based on factor graph optimization often lack sufficient adaptability. Common issues include backend optimization latency leading to odometry state divergence, and performance degradation in the scan-to-map matching mechanism due to local map bloat when operat...
Baocun Wang, Quan-Yu Wu, Xiao-Dong Lu et al.· International Journal of Com...· 0 citations
This review presents an architecture-centered synthesis of LiDAR-based SLAM, covering LiDAR-only and LiDAR-inertial systems through a dual-axis, architecture-centered taxonomy, and highlights the field’s progression toward continuous-time estimation, semantic scene understanding, adaptive map representations, neural im...
E. Muhammed, A. Shaker· Italian National Conference...· 0 citations
The proposed GNSS/INS/Mamba-ODO framework learns a direct and efficient mapping from raw IMU outputs to 3D velocity through asymmetric sequence segmentation, multi-scale temporal modeling, and a state-space sequence learning backbone.
Accurate localization and mapping are essential for autonomous mobile robots operating in unknown environments. This study investigates the impact of Extended Kalman Filter (EKF)-based sensor fusion on the performance of three widely used two-dimensional (2D) LiDAR Simultaneous Localization and Mapping (SLAM) algorithm...
Christian Merrick, V. Nandikolla· Italian National Conference...· 0 citations
The airborne laser scanning (ALS) system is an efficient 3D spatial data acquisition system that plays a key role in complex environmental mapping. Traditional ALS systems primarily depend on GNSS/INS integrated navigation to compute high-precision poses, with LiDAR used only for measuring accurate distances from the s...
Pei-Dong Yu, Yuan-Jun Chen, Meng-Kui Liu et al.· International Conference on...· 0 citations
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