吉林大学学报（信息科学版）2025，Vol.43Issue(5)：1006-1013,8.
基于改进YOLACT++的语义SLAM系统
Semantic SLAM System Based on Improved YOLACT++
摘要
Abstract
SLAM(Simultaneous Localization and Mapping)technology is a camera pose estimation based on static scene features.It is susceptible to dynamic objects in the process of feature calculation and matching at its front end.Therefore a method of instance segmentation combined with multi-view geometric constraints is proposed to improve the front-end feature processing of visual SLAM and eliminate the interference of dynamic information.Specifically,in the front end of the ORB-SLAM3(Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping3)framework,the YOLACT++(You Only Look At CoefficienTs++)instance segmentation thread is paralleled,and the segmented results are used to supplement the multi-view geometric constraint method testing the dynamic consistency of feature points.The EfficientNetV2 network is used to replace the original backbone network of YOLACT++,and the TensorRT is used to quantify the instance segmentation model to reduce the front-end computing pressure of the algorithm.The test of TUM data set shows that the positioning accuracy of the proposed algorithm in high dynamic environment is 80.6％higher than that of ORB-SLAM3 algorithm.关键词
语义SLAM/YOLACT++分割算法/多视几何约束/动态场景Key words
semantic simultaneous localization and mapping(SLAM)/YOLACT++segmentation algorithm/multi-view geometric constraints/dynamic scene分类
计算机与自动化引用本文复制引用
任伟建,沈文旭,任璐,张永丰..基于改进YOLACT++的语义SLAM系统[J].吉林大学学报（信息科学版）,2025,43(5):1006-1013,8.基金项目
国家自然科学基金资助项目(61933007) （61933007）
河北省自然科学基金面上资助项目(D2022107001) （D2022107001）
