Aug 2026· 2026 IEEE International Conference on Mechatronics and Automation (ICMA)· pp. 939-946· 0 citations· 15 references
Abstract
As a cutting-edge technology in the AGV industry, 3D lidar SLAM is widely applied in the navigation of wheeled robots due to its ability to provide precise and robust positioning for robots. Compared with 2D lidar SLAM, 3D SLAM often requires more computing resources. This paper proposes a novel method for extracting 3D features from point clouds based on non-repetitive scanning lidar. The 3D feature point clouds are projected into two dimensions, and the improved RANSAC algorithm is applied to a 2D point cloud, realizing robust 2D point cloud registration of unstable feature points. The algorithm was tested on forklift AGV platform. The results show that this method can extract feature points in both indoor and outdoor environments, providing odometry output, while consuming relatively limited computing resources. This research provides a novel approach for the implementation of real-time lidar-IMU odometry.
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
LiDAR-based SLAM (Simultaneous Localization and Mapping) and LIO (LiDAR-inertial odometry) algorithms are often used for precise navigation of unmanned aerial vehicles, especially during interactions with the aerial robot's environment. However, the performance of these algorithms is greatly dependent on the scenario,...
Robert Milijaš, J. R. M. Dios, Stjepan Bogdan· 0 citations
The advancement of robotics has expanded applications across various sectors, increasing the need for reliable mapping and navigation in unfamiliar environments. Simultaneous Localization and Mapping (SLAM) enables mobile robots to estimate their position while constructing an environmental map, while RGB-D SLAM combin...
Fahmizal, Priyova Muhammad Rafief, Rico Agustiawan et al.· Applied Sciences· 0 citations
Backpack and handheld LiDAR simultaneous localization and mapping (SLAM) systems have become an important solution for large-scale 3D data acquisition. Since Global Navigation Satellite System (GNSS) positioning is not always available in many LiDAR SLAM systems, point clouds acquired from different surveying projects...
Hua Liu, Jie Dong, Bo Liu· Applied Sciences· 0 citations
This study aims to develop a practical LiDAR–inertial Simultaneous Localization and Mapping (SLAM) system for industrial mobile robots operating in dynamic, repetitive and resource-constrained warehouse environments. Rather than treating dynamic object handling as isolated point removal, the proposed system formula...
Lin-Kui Wu, Rui-Han Bai, Yi-Xuan Du et al.· Industrial robot· 0 citations
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