一种基于因子图消元优化的激光雷达视觉惯性融合SLAM方法
An Fusion SLAM Method for LiDAR Visual and IMU Based on Factor Map Elimination Optimization
查看参考文献26篇
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文摘
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针对单一传感器SLAM(Simultaneous Localization And Mapping)技术在复杂环境中存在精度低、可靠性差等问题,提出一种基于因子图消元优化的激光雷达、视觉和IMU(Inertial Measurement Unit)融合SLAM算法(Multi Factor Graph fusion SLAM with IMU as the Dominant system,ID-MFG-SLAM).首先,采用多因子图模型,提出以IMU为主系统,视觉与激光雷达为辅系统,通过引入辅系统观测因子约束IMU偏差,并接收IMU里程计因子实现运动预测与融合的全新结构.之后,为降低融合后的优化成本,加入滑窗机制并设计基于Householder变换的QR分解消元法将因子图转换为贝叶斯网络.最后,引入一种球面线性插值与线性插值之间的自适应插值算法,将激光雷达点云投影到单位球体上实现视觉特征点深度估计.实验结果表明,相比其他经典算法,该方法在复杂大、小场景中绝对轨迹误差分别可达到约0.68 m和0.24 m,具有更高的精度和可靠性. |
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其他语种文摘
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Addressing the limitations of single-sensor SLAM (Simultaneous Localization And Mapping) techniques, degraded perception, and poor reliability in complex environments, this paper proposes a multi-factor graph fusion SLAM algorithm with IMU as the dominant system (ID-MFG-SLAM). Firstly, the utilization of a multi-factor graph model, with the IMU (Inertial Measurement Unit) as the primary system and visual and LIDAR sensors as secondary systems. This novel structure incorporates observation factors from the secondary systems to constrain IMU biases and integrates IMU odometry factors for motion prediction and fusion. To reduce the optimization cost after fusion, a sliding window mechanism is introduced for historical state information backtracking. Additionally, a QR decomposition elimination method based on Householder transformation is employed to convert the factor graph into a Bayesian network, simplifying the graph's structure and improving computational efficiency. Furthermore, an adaptive interpolation algorithm between quaternion spherical linear interpolation and linear interpolation is introduced. This algorithm projects LIDAR point clouds onto a unit sphere, enabling depth estimation of visual feature points. The experimental results show that compared to other classic algorithms, this method can achieve absolute trajectory errors of about 0.68 m and 0.24 m in complex large and small scenes, respectively, with higher accuracy and reliability. |
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来源
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电子学报
,2023,51(11):3042-3052 【核心库】
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DOI
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10.12263/DZXB.20230209
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关键词
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同时定位与建图
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多传感器融合
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复杂场景
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激光雷达
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IMU里程计
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因子图优化
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地址
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1.
内蒙古工业大学电力学院, 内蒙古, 呼和浩特, 010080
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内蒙古自治区高等学校智慧能源技术与装备工程研究中心, 内蒙古自治区高等学校智慧能源技术与装备工程研究中心, 内蒙古, 呼和浩特, 010080
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大规模储能技术教育部工程研究中心, 大规模储能技术教育部工程研究中心, 内蒙古, 呼和浩特, 010080
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金
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内蒙古科技攻关项目
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内蒙古自然科学基金
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文献收藏号
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CSCD:7641759
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