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{\deg}/m, with an average run time of 53 ms.

翻译：本文提出了一种新颖的激光雷达里程计估计方法，该方法完全使用对偶四元数对系统进行参数化。为实现此目标，从点云提取的特征（包括边缘、平面和稳定三角形描述符（STD））以及优化问题均在对偶四元数集合中表达。这种方法能够通过对偶四元数运算直接结合平移与旋转误差，从而显著提升了位姿估计性能，这在与其它先进方法的对比实验中得到了验证。相较于其他仅使用激光雷达的里程计方法，我们的方法降低了漂移误差，尤其是在存在急转弯和具有大角度位移的剧烈运动场景中。DualQuat-LOAM 在多个公开数据集上进行了基准测试。在 KITTI 数据集中，其平移和旋转误差分别为 0.79% 和 0.0039{\deg}/m，平均运行时间为 53 毫秒。