LPVIMO-SAM: 通过平滑与映射实现紧密耦合的激光雷达/偏振视觉/惯性/磁力计/光学流里程计
LPVIMO-SAM: Tightly-coupled LiDAR/Polarization Vision/Inertial/Magnetometer/Optical Flow Odometry via Smoothing and Mapping
AI总结：
LPVIMO-SAM通过融合激光雷达、偏振视觉、惯性、磁力计和光学流，实现高鲁棒性的实时里程计与地图构建。
摘要：
我们提出了一种通过平滑与映射实现紧密耦合的激光雷达/偏振视觉/惯性/磁力计/光学流里程计（LPVIMO-SAM）框架，该框架集成了激光雷达、偏振视觉、惯性测量单元、磁力计和光学流，实现高精度和高鲁棒性的实时状态估计和地图构建。在挑战性环境中，如激光雷达退化、低纹理区域和特征稀少区域，该框架能够提供稳定的表现。LPVIMO-SAM包含两个子系统：偏振视觉-惯性系统和激光雷达/惯性/磁力计/光学流系统。偏振视觉通过提取场景的偏振信息，增强了低特征和低纹理场景下的视觉/惯性里程计的鲁棒性。磁力计获取航向角，光学流获取速度和高度以减少累积误差。设计了磁力计航向先验因子、光学流速度观测因子和高度观测因子，通过因子图优化消除激光雷达/惯性里程计的累积误差。同时，LPVIMO-SAM即使在两个子系统之一失效时也能保持稳定的定位，进一步扩展了其在激光雷达退化、低纹理和低特征环境中的适用性。代码可在https://github.com/junxiaofanchen/LPVIMO-SAM上获取。
英文摘要：
We propose a tightly-coupled LiDAR/Polarization Vision/Inertial/Magnetometer/Optical Flow Odometry via Smoothing and Mapping (LPVIMO-SAM) framework, which integrates LiDAR, polarization vision, inertial measurement unit, magnetometer, and optical flow in a tightly-coupled fusion. This framework enables high-precision and highly robust real-time state estimation and map construction in challenging environments, such as LiDAR-degraded, low-texture regions, and feature-scarce areas. The LPVIMO-SAM comprises two subsystems: a Polarized Vision-Inertial System and a LiDAR/Inertial/Magnetometer/Optical Flow System. The polarized vision enhances the robustness of the Visual/Inertial odometry in low-feature and low-texture scenarios by extracting the polarization information of the scene. The magnetometer acquires the heading angle, and the optical flow obtains the speed and height to reduce the accumulated error. A magnetometer heading prior factor, an optical flow speed observation factor, and a height observation factor are designed to eliminate the cumulative errors of the LiDAR/Inertial odometry through factor graph optimization. Meanwhile, the LPVIMO-SAM can maintain stable positioning even when one of the two subsystems fails, further expanding its applicability in LiDAR-degraded, low-texture, and low-feature environments. Code is available on https://github.com/junxiaofanchen/LPVIMO-SAM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
