A Multi-Layered 3D NDT Scan-Matching Method for Robust Localization in Logistics Warehouse Environments

This paper introduces the Kabsch Marker Estimation Algorithm (KMEA), a new, robust multimarker localization method designed for Autonomous Mobile Robots (AMRs) within Industry 4.0 (I4.0) settings. By integrating the Kabsch Algorithm, our approach significantly enhances localization robustness by aligning detected fiducial markers with their known positions. Unlike conventional methods that rely on a limited subset of visible markers, the KMEA uses all available markers, without requiring the camera's extrinsic parameters, thereby improving robustness. The algorithm was validated in an I4.0 automated warehouse mockup, with a four-stage methodology compared to a previously established marker estimation algorithm for reference. On the one hand, the results have demonstrated the KMEA's similar performance in standard controlled scenarios, with millimetric precision across a set of error metrics and a mean relative error (MRE) of less than 1%. On the other hand, KMEA, when faced with challenging test scenarios with outliers, showed significantly superior performance compared to the baseline algorithm, where it maintained a millimetric to centimetric scale in error metrics, whereas the other suffered extreme degradation. This was emphasized by the average reduced results of error metrics from 86.9% to 92% in Parts III and IV of the test methodology, respectively. These results were achieved using low-cost hardware, indicating the possibility of even greater accuracy with advanced equipment. The paper details the algorithm's development, theoretical framework, comparative advantages over existing methods, discusses the test results, and concludes with comments regarding its potential for industrial and commercial applications by its scalability and reliability.INDEX TERMS ArUco markers, automated warehouse, autonomous mobile robots, fiducial markers, ground truth system, industry 4.0, indoor localization.

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A Novel Point Cloud Registration Method for Multimedia Communication in Automated Driving Metaverse

Yong,

Lei,

Shen

et al. 2024

ACM Trans. Internet Technol.

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The development of the Metaverse offers more possibilities for autonomous driving. This is mainly reflected in the fact that the scene reconstructed based on multiple sensors can help the autonomous vehicle establish a Metaverse world based on its own real situation. When multiple vehicles build such a Metaverse world in the same scene, they can exchange their information including the perception of the vehicle’s condition and the surrounding environment, which means the creation of a Metaverse world containing all vehicles. Thus, an indirect Human-Human Multimedia Communications based on the Metaverse is realized, in which the autonomous driving system acts as a multimedia to provide a medium for the exchange of information between vehicles. The establishment of the Metaverse is based on stable and high-quality scene reconstruction. Accurate scene information can bring high quality Human-Human Multimedia Communications. Achieving this requires accurate scene reconstruction and vehicle positioning, both of which depend on accurate point cloud registration. In this work, we propose a robust registration method that is based on semantic information and scaling constraints. Our method consists of three steps. Firstly, we filter out points that might mislead the point cloud registration process by leveraging semantic information. Secondly, we obtain a more accurate initial matrix using TEASER++, which is based on semantic information and feature descriptors. Finally, we use semantic information and scaling to constrain the nearest neighbor matching and filter out error matches to obtain a higher quality registration. By following these steps, our method overcomes the memory problem faced by TEASER++ when there are large-scale point clouds, and greatly reduces its running time. Meanwhile, our algorithm achieved superior point cloud registration results compared to two state-of-the-art robust registration techniques: Globally optimal Iterative Closest Point (Go-ICP) and Generalized Iterative Closest Point (GICP).

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A Multi-Layered 3D NDT Scan-Matching Method for Robust Localization in Logistics Warehouse Environments

Cited by 3 publications

References 19 publications

Design of a logistics warehouse robot positioning and recognition model based on improved EKF and calibration algorithm

Design of a logistics warehouse robot positioning and recognition model based on improved EKF and calibration algorithm

Kabsch Marker Estimation Algorithm—A Multi-Robot Marker-Based Localization Algorithm Within the Industry 4.0 Context

A Novel Point Cloud Registration Method for Multimedia Communication in Automated Driving Metaverse

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