文献类型： 外文期刊

作者： Zhang, Zhaobo ¹ ; Yuan, Yanwei ¹ ; Dong, Xin ¹ ; Yuan, Yulong ¹ ; An, Sa ¹ ; Cao, Yue ¹ ; Li, Yang ¹ ; Chen, Yuefeng ³ ;

作者机构： 1.State Key Lab Agr Equipment Technol, Beijing 100083, Peoples R China

2.Chinese Acad Agr Mechanizat Sci Grp Co Ltd, Beijing 100083, Peoples R China

3.Chinese Acad Agr Mechanizat Sci Grp Co Ltd, Zhejiang Branch, Hangzhou 030006, Peoples R China

关键词： chicken house breeding; autonomous navigation; mapping method; point cloud matching

期刊名称：SENSORS （ 影响因子：3.5； 五年影响因子：3.7 ）

ISSN：

年卷期： 2025 年 25 卷 9 期

页码：

收录情况： SCI

摘要： Highlights This article proposes an improved gmapping algorithm by combining an optimized ICP point cloud matching algorithm and a classification resampling method, which solves the problems of the insufficient number of effective particles, high repetition rate of environmental map information particles, and penetration phenomenon in the process of constructing maps in multi-layer cage architecture chicken coops. What are the main findings? Improved ICP point cloud matching algorithm. Method of classification resampling based on the weight of point cloud data. What is the implication of the main finding? It significantly improved point cloud matching accuracy and increased the number of effective particles. It ensured the diversity of effective particles and solved the problem of the penetration phenomenon in the mapping process of the chicken coop environment.Highlights This article proposes an improved gmapping algorithm by combining an optimized ICP point cloud matching algorithm and a classification resampling method, which solves the problems of the insufficient number of effective particles, high repetition rate of environmental map information particles, and penetration phenomenon in the process of constructing maps in multi-layer cage architecture chicken coops. What are the main findings? Improved ICP point cloud matching algorithm. Method of classification resampling based on the weight of point cloud data. What is the implication of the main finding? It significantly improved point cloud matching accuracy and increased the number of effective particles. It ensured the diversity of effective particles and solved the problem of the penetration phenomenon in the mapping process of the chicken coop environment.Highlights This article proposes an improved gmapping algorithm by combining an optimized ICP point cloud matching algorithm and a classification resampling method, which solves the problems of the insufficient number of effective particles, high repetition rate of environmental map information particles, and penetration phenomenon in the process of constructing maps in multi-layer cage architecture chicken coops. What are the main findings? Improved ICP point cloud matching algorithm. Method of classification resampling based on the weight of point cloud data. What is the implication of the main finding? It significantly improved point cloud matching accuracy and increased the number of effective particles. It ensured the diversity of effective particles and solved the problem of the penetration phenomenon in the mapping process of the chicken coop environment.Abstract This study delves into the mapping method for the navigation system of a chicken coop disinfection robot. It systematically analyzes the problems of insufficient effective particle count, high particle repetition rate in environmental map information, and penetration phenomenon in traditional SLAM laser point cloud mapping technology in chicken coop environments containing multiple layers of chicken cages. To address these issues, this paper proposes an optimized mapping method based on an improved ICP algorithm, significantly improving the laser point clouds' registration performance. At the same time, by limiting the sampling of environmental map information particles within a specific range and optimizing the screening based on the predicted distribution of particle poses and the matching degree of the map, the diversity of particles and the accuracy of map information have been effectively improved. The field experiment results show that the maximum error of this method on the chicken coop environment map does not exceed 3.5 cm. The environmental characteristics of the chicken coop are maximally preserved, which verifies the effectiveness and robustness of this method and provides a scientific basis for the mapping method of the livestock and poultry breeding robot navigation system.