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First, a curved-voxel-based intensity\u2013geometry joint probability model is constructed to transform conventional point features into spatial features capable of effectively capturing local structural patterns. These features are then aligned by minimizing the joint probability distance using the normal distributions transform, thereby enhancing feature discriminability and registration stability in highly repetitive scenes. Second, a maximum a posteriori-based recursive noise estimator is designed. By employing a dual sub-filter architecture, the proposed estimator enables joint modeling and online optimization of the noise parameters associated with LiDAR odometry and the inertial measurement unit, thereby improving the system\u2019s adaptability to terrain-induced perturbations and sensor uncertainties. Experimental results demonstrate that the proposed method achieves a mean relative translation error of 1.42% and a mean relative rotation error of 1.53\u00b0\/100 m in agricultural scenarios. In addition, the average error in row spacing estimation derived from the reconstructed map is constrained within 0.071 m. Compared with baseline methods, the proposed system exhibits significant advantages in both pose estimation accuracy and agronomic structural perception capability.<\/jats:p>","DOI":"10.1017\/s0263574726103397","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T06:30:16Z","timestamp":1776839416000},"page":"846-864","source":"Crossref","is-referenced-by-count":0,"title":["A robust simultaneous localization and mapping method for agricultural environments: integrating spatial modeling and noisy estimation"],"prefix":"10.1017","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8139-5500","authenticated-orcid":false,"given":"Ziyang","family":"Wang","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00f1zfq44","id-type":"ROR","asserted-by":"publisher"}],"name":"State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenfu","family":"Tong","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00f1zfq44","id-type":"ROR","asserted-by":"publisher"}],"name":"State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haibo","family":"Zhou","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00f1zfq44","id-type":"ROR","asserted-by":"publisher"}],"name":"State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingjun","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Institute of Modern Agricultural Equipment, Guangzhou"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaozhuo","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Electromechanical Information Technology, Xi'an"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biyao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Electromechanical Information Technology, Xi'an"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"S0263574726103397_ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2022.3169442"},{"key":"S0263574726103397_ref36","doi-asserted-by":"crossref","unstructured":"[36] Wu, Y. , Guadagnino, T. , Wiesmann, L. , Klingbeil, L. , Stachniss, C. and Kuhlmann, H. , \u201cLIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters,\u201d In: 2024 IEEE International Conference on Robotics and Automation (ICRA) (2024) pp. 13741\u201313747.","DOI":"10.1109\/ICRA57147.2024.10610667"},{"key":"S0263574726103397_ref5","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy14102365"},{"key":"S0263574726103397_ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3361092"},{"key":"S0263574726103397_ref22","doi-asserted-by":"crossref","unstructured":"[22] Park, S. , Wang, S. , Lim, H. and Kang, U. , \u201cCurved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance,\u201d In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019) pp. 6459\u20136464.","DOI":"10.1109\/IROS40897.2019.8968026"},{"key":"S0263574726103397_ref2","first-page":"16051","article-title":"Multisensors fusion SLAM-aided forest plot mapping with backpack dual-LiDAR system","volume":"17","author":"Yang","year":"2024","journal-title":"IEEE J. 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