农机使用与维修Issue(1)：1-10,10.DOI:10.14031/j.cnki.njwx.2026.01.001
基于多传感融合目标检测的动态物剔除SLAM算法
Dynamic object removal SLAM algorithm based on multi-sensor fusion target detection
摘要
Abstract
To address the issue that the positioning accuracy and mapping quality of Simultaneous Localization And Map-ping(SLAM)algorithms degrade due to interference from dynamic goose flocks on feed delivery mobile robots in modern goose farming scenarios,a dynamic SLAM algorithm based on multi-sensor fusion target detection is proposed.This al-gorithm is built on the LIO-SAM framework,integrating LiDAR and inertial measurement unit(IMU)to construct the SLAM system,and adopts a front-end and back-end architecture to optimize positioning and mapping performance.It uses the Hungarian algorithm to track the movement state of goose flocks in real time,and combines with multi-sensor fusion target detection algorithm to accurately identify and eliminate feature points generated by dynamic goose flocks,ef-fectively reducing positioning and mapping errors.Tested on public datasets such as KITTI and UrbanNav as well as ac-tual farming scenario data,in the KITTI07 sequence,the root mean square error(RMSE)is reduced by 33.18％com-pared with classic algorithms like LeGO-LOAM,LIO-SAM and LVI-SAM.In actual goose farming environments,it can quickly filter out dynamic goose flock interference,improving mapping quality and navigation reliability.This re-search provides a new technical solution for intelligent feed delivery in goose farming and promotes the development of animal husbandry automation.关键词
多传感融合/定位与地图构建(SLAM)/动态物体剔除/紧耦合策略Key words
multi-sensor fusion/Simultaneous Localization And Mapping(SLAM)/dynamic object removal/tight cou-pling strategy分类
信息技术与安全科学引用本文复制引用
荣艺涵,杨坚,张燕军,陈爱军,陈彪..基于多传感融合目标检测的动态物剔除SLAM算法[J].农机使用与维修,2026,(1):1-10,10.基金项目
江苏省大学生创新创业计划项目(202411117111Y) （202411117111Y）
