Eigen 即所需：具有内部关联的高效激光雷达-惯性连续时间里程计
Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry with Internal Association
AI总结：
本文提出 SLICT2 连续时间激光雷达-惯性里程计，利用线性求解器与即时特征关联实现高效实时定位，在动态场景下性能优异。
摘要：
本文提出了一种名为 SLICT2 的连续时间激光雷达-惯性里程计（CT-LIO）系统，该系统提出了两个主要见解。第一，与传统观点相反，CT-LIO 算法可以通过线性求解器在仅几次迭代内完成优化，这比常用的非线性求解器更高效。第二，CT-LIO 从正确的关联中获益比从迭代次数中获益更多。基于这些思想，我们使用一个定制求解器来实现我们的方法，其中特征关联过程在每个增量步骤之后立即执行，并且解可以在几次迭代内收敛。我们的实现能够在高密度控制点下实现实时性能，同时在高度动态的运动场景中产生具有竞争力的性能。我们通过与其他现有最先进的 CT-LIO 方法进行比较，展示了我们方法的优势。源代码将发布以造福社区。
英文摘要：
In this paper, we propose a continuous-time lidar-inertial odometry (CT-LIO) system named SLICT2, which promotes two main insights. One, contrary to conventional wisdom, CT-LIO algorithm can be optimized by linear solvers in only a few iterations, which is more efficient than commonly used nonlinear solvers. Two, CT-LIO benefits more from the correct association than the number of iterations. Based on these ideas, we implement our method with a customized solver where the feature association process is performed immediately after each incremental step, and the solution can converge within a few iterations. Our implementation can achieve real-time performance with a high density of control points while yielding competitive performance in highly dynamical motion scenarios. We demonstrate the advantages of our method by comparing with other existing state-of-the-art CT-LIO methods. The source code will be released for the benefit of the community.
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。
