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.

翻译：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，该系统阐明了两个主要观点。其一，与常规认知相反，CT-LIO算法可以通过线性求解器在仅少数几次迭代中得到优化，这比常用的非线性求解器更为高效。其二，相比于迭代次数，CT-LIO从正确的特征关联中获益更多。基于这些思路，我们采用定制化的求解器实现了所提方法，其中特征关联过程在每次增量步骤后立即执行，且解可在少数几次迭代内收敛。我们的实现能够在控制点高密度的情况下达到实时性能，同时在高度动态的运动场景中展现出具有竞争力的性能。我们通过与现有其他先进的CT-LIO方法进行对比，证明了所提方法的优势。源代码将公开发布以惠及研究社区。