@article{Yang2026, 
author = {Lili Yang and Ziang Li and Xiao Guo and Jiale LI and Xinyun Zhao and Caicong Wu},
title = {AgriLoc: Temporal-enhanced LiDAR localization method for agricultural scenarios},
year = {2026},
journal = {Transactions of the Chinese Society of Agricultural Engineering},
volume = {42},
number = {15},
pages = {234-242},
keywords = {deep learning, LiDAR, agricultural machinery localization, smart agriculture, scene coordinate regression},
url = {https://www.sciopen.com/article/10.11975/j.issn.1002-6819.202601206},
doi = {10.11975/j.issn.1002-6819.202601206},
abstract = {Agricultural scenarios, such as tractor roads, village and farm paths, can often exhibit sparse geometric structure, weak vertical constraints, and high scene repetition. Since existing scene coordinate regression (SCR) methods are suitable for structured urban environments, they often suffer from unstable pose estimation in agricultural scenes. A single LiDAR frame can also contain insufficient discriminative information for accurate localization. This study aims to improve the localization accuracy and deployment efficiency of autonomous agricultural machinery using deep learning and LiDAR localization. AgriLoc was proposed as a temporal-enhanced LiDAR localization framework for rapid adaptation to new agricultural scenes with the high efficiency of frozen-backbone SCR pipelines. The localization strategy was selected to freeze a pre-trained scene-agnostic backbone and then train only scene-related prediction modules, thereby reducing the amount of scene-specific optimization during deployment. On top of this framework, AgriLoc also introduced a temporal sample classification guidance (T-SCG) and a multi-head attention enhancement module to exploit temporal context from consecutive LiDAR frames. Specifically, a sequence of LiDAR point clouds was processed to produce both global feature sequences and current-frame spatial dense features. A pre-trained classification head mapped the global features into region-discriminative guidance features, and then temporally encoded them by a GRU-based T-SCG module to capture motion continuity and suppress ambiguity caused by sparse or repetitive structures. A multi-head attention mechanism was applied to the full history of guided states. The forgetting effect of recurrent modeling over long sequences was further alleviated to adaptively focus on keyframes with stronger scene discriminability. The temporal context was fused with current-frame spatial dense features and then fed into the regression head to predict scene coordinates, which are then used by RANSAC to estimate the final 6-DoF pose. Experiments were conducted on a self-built dataset. A 9 km route was selected to cover the full operational chain of agricultural machinery, including departure from the machine shed, traversal through village roads and public roads, entry into farmland, field operation, and return to the shed. Two sequences were collected on the same route at time periods, with 30.4 GB (15 611 frames) and 23.0 GB (12 017 frames), respectively. Experimental results showed that AgriLoc achieved a mean translation error of 2.17 m and a mean rotation error of 2.75°. Compared with the baseline LightLoc, AgriLoc reduced the average translation error by 27.2% and the average rotation error by 6.4% in challenging tractor-road scenes, while only 3.8 h of training was required for a new scene. An ablation study further showed that the T-SCG reduced the mean translation error from 2.98 to 2.50 m, and multi-head attention was further improved to 2.17 m, indicating the complementary contribution of temporal guidance and attention after keyframe selection. Furthermore, cross-scene robustness of AgriLoc was evaluated on the NCLT public and campus dataset. The high cross-scene adaptability of AgriLoc was achieved, with a mean translation error of 1.88 m and a mean rotation error of 2.82° on NCLT, while 1.84 m translation error and 2.73° rotation error on the campus dataset. Overall, this finding can also provide a practical, high-precision, and rapidly deployable LiDAR localization for autonomous navigation of agricultural machinery.}
}