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：本文提出一种名为SLICT2的连续时间激光雷达-惯性里程计（CT-LIO）系统，其核心贡献包含两点：其一，与传统认知相反，CT-LIO算法可通过线性求解器在极少迭代次数内完成优化，其效率远超常规非线性求解器；其二，CT-LIO的性能提升更多依赖于正确关联而非迭代次数。基于上述思路，我们采用定制化求解器实现该方法：在每次增量步骤后立即执行特征关联过程，使得解能在数次迭代内收敛。本实现可在高密度控制点条件下达到实时性能，同时在强动态运动场景中保持竞争力。通过与现有先进CT-LIO方法的对比，我们验证了该方法的优势。相关源代码将开源以惠及学术界。