用双四元数统一表示位姿，提升激光里程计精度
DualQuat-LOAM: LiDAR Odometry and Mapping parametrized on Dual Quaternions
- 全系统基于双四元数参数化，直接融合平移与旋转误差
- 在KITTI数据集上实现0.79%位移误差、0.0039°/m转角误差
- 适合高动态场景下的实时定位，尤其擅长弯道和急转弯
本文提出一种全新的激光里程计方法，完全采用双四元数对系统进行参数化。通过将点云特征（包括边缘、平面及稳定三角形描述符，STD）与优化问题均表达在双四元数空间中，实现了平移与姿态误差的直接联合优化。该方法显著提升了位姿估计性能，在对比实验中优于其他先进方法。尤其在急转弯和大角度运动场景下，相比其他纯激光里程计方法，有效降低了漂移误差。本方法在多个公开数据集上进行了评测，在KITTI数据集上达到0.79%的位移误差和0.0039°/m的转角误差，平均运行时间为53毫秒。
原文摘要 · Abstract (English)
This paper reports on a novel method for LiDAR odometry estimation, which completely parameterizes the system with dual quaternions. To accomplish this, the features derived from the point cloud, including edges, surfaces, and Stable Triangle Descriptor (STD), along with the optimization problem, are expressed in the dual quaternion set. This approach enables the direct combination of translation and orientation errors via dual quaternion operations, greatly enhancing pose estimation, as demonstrated in comparative experiments against other state-of-the-art methods. Our approach reduced drift error compared to other LiDAR-only-odometry methods, especially in scenarios with sharp curves and aggressive movements with large angular displacement. DualQuat-LOAM is benchmarked against several public datasets. In the KITTI dataset it has a translation and rotation error of 0.79% and 0.0039°/m, with an average run time of 53 ms.
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