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管玲,李丹,李俊祥,等. 基于改进点线特征与惯性传感单元融合的SLAM算法[J]. 安徽工业大学学报(自然科学版),2024,41(3):305-313. doi: 10.12415/j.issn.1671-7872.23165
引用本文: 管玲,李丹,李俊祥,等. 基于改进点线特征与惯性传感单元融合的SLAM算法[J]. 安徽工业大学学报(自然科学版),2024,41(3):305-313. doi: 10.12415/j.issn.1671-7872.23165
GUAN Ling, LI Dan, LI Junxiang, LU Yu. SLAM Algorithm Based on Fusion of Improved Point and Line Features with Inertial Sensing Units[J]. Journal of Anhui University of Technology(Natural Science), 2024, 41(3): 305-313. DOI: 10.12415/j.issn.1671-7872.23165
Citation: GUAN Ling, LI Dan, LI Junxiang, LU Yu. SLAM Algorithm Based on Fusion of Improved Point and Line Features with Inertial Sensing Units[J]. Journal of Anhui University of Technology(Natural Science), 2024, 41(3): 305-313. DOI: 10.12415/j.issn.1671-7872.23165

基于改进点线特征与惯性传感单元融合的SLAM算法

SLAM Algorithm Based on Fusion of Improved Point and Line Features with Inertial Sensing Units

  • 摘要: 针对传统同步定位与建图(SLAM)算法在密集环境中过度提取线段的问题,提出1种基于改进点线特征与惯性传感单元(IMU)紧耦合的SLAM算法,即IPLI-SLAM。在数据预处理阶段采用Shi-Tomasi特征进行点特征提取并使用LK (Lucas-Kanade)光流进行跟踪和匹配;引入线特征,在像素梯度过滤机制的基础上改进直线段检测(LSD)提取算法,筛选过滤线特征密集区域;将视觉点线信息与IMU紧耦合后加入后端,提高算法精度,最后将IPLI_SLAM算法在数据集EuRoc与实际场景下进行测试验证。结果表明:相较于LSD算法,改进LSD算法的提取及匹配时间减少8.2%;相较VINS-mono与PL-VINS 算法,IPLI-SLAM算法定位精度分别提高50.7%,13.2%,在运动速度快、场景模糊的环境中定位精度分别提高55.6%,25.1%;在实际多纹理场景中本文算法定位轨迹起点与终点相差的距离均小于VINS-mono算法,由此证明本文算法在显著提高定位精度的同时,还具有更高的稳定性和更强的鲁棒性。

     

    Abstract: In response to the issue of excessive line segment extraction by traditional simultaneous localization and mapping (SLAM) algorithms in dense environments, a SLAM algorithm based on improved point line features and tightly coupled inertial sensing units (IMU) was proposed, namely IPLI-SLAM. During the data preprocessing phase, Shi-Tomasi features were used for point feature extraction, and the Lucas-Kanade (LK) optical flow was used for tracking and matching. Line features were introduced to improve the line segment detector (LSD) extraction algorithm based on pixel gradient filtering mechanism, and to filter out areas with dense line features. By tightly coupling visual point and line information with IMU and adding it to the backend, the algorithm accuracy was improved. Finally, IPLI-SLAM algorithm was tested and validated on the dataset EuRoc and in actual scenes.The results show that compared with the original LSD algorithm, the improved LSD algorithm reduces extraction and matching time by 8.2%. Compared with the VINS-mono and PL-VINS algorithms, the IPLI-SLAM algorithm has improved positioning accuracy by 50.7% and 13.2%, respectively. In environments with fast motion speed and blurred scenes, the positioning accuracy is further enhanced by 55.6% and 25.1%, respectively. In actual multi-texture scenes, the distance between the starting and ending points of the localization trajectory using our algorithm is smaller than that of VINS-mono algorithm. This proves that the algorithm proposed in this article not only significantly improves positioning accuracy, but also has higher stability and stronger robustness.

     

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