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| 基于SLAM的弱纹理环境车辆定位 |
| SLAM-based vehicle localization in weakly textured environments |
| 投稿时间：2024-03-16 修订日期：2024-04-15 |
| DOI： |
| 中文关键词: 弱纹理环境 智能车定位 SLAM 特征融合 |
| 英文关键词: weak textured environments intelligent vehicle localization SLAM features fusion |
| 基金项目: |
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| 摘要点击次数: 14 |
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| 中文摘要: |
| 特征的准确获取对于视觉SLAM系统的位姿估计具有重要影响,在类地下停车场弱纹理环境下,鲁棒的特征提取具有较大挑战。基于此,本文提出了一种弱纹理环境下的点线融合特征提取方法,藉此提高视觉惯性系统的定位性能。首先,为获得稳定视觉特征输入,基于传统线特征检测算法通过参数调优、线段合并及筛选策略在提升图像特征检测效率的同时获得弱纹理环境下的鲁棒线特征。其次,基于滑动窗和边缘化策略非线性优化框架,联合图像特征点/线重投影误差、IMU测量误差以及边缘化先验约束构造联合优化函数,实现视觉与惯性信息的紧耦合。最后,EuRoC公开数据集上的实验结果表明,相对于传统视觉SLAM方案,本文所提出方法的定位精度及鲁棒性均有较大优势。 |
| 英文摘要: |
| The accurate extraction of feature points has a significant impact on the pose estimation of visual SLAM system, in weak textured environments such as underground parking lots, robust feature point extraction poses significant challenges. In order to solve the problem, this paper proposes a weak texture environment feature extraction method that combines point and line features to improve the localization performance of visual inertial systems (PIL-VIO). Firstly, to obtain stable visual input features, the traditional line feature detection algorithm is improved by parameter tuning, line segment merging, and length-based filtering strategies, which enhances the efficiency and robustness of line feature detection. Secondly, in order to realize the tight coupling of visual and inertial information, this paper constructs a joint optimization function by combining the line features reprojection error with the point features reprojection error, IMU measurement errors, and marginalized prior constraints in a nonlinear optimization framework based on sliding window and marginalization strategies. Finally, the experimental results on the EUROC dataset show that, compared to traditional visual localization algorithms, the method proposed in this paper has significant advantages in terms of system positioning accuracy and robustness. |
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