摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-研究人员详细介绍了机器人S-机器人和自动化的新数据。根据NewsRx编辑的新加坡Singa Pore的新闻报道，研究人员称：“在这封信中，我们提出了一个名为SLICT2的连续US时间LIDAR-惯性里程计(CT-LIO)系统，它促进了两个主要见解。第一，与传统观点相反，CT-LIO算法只需几次迭代就可以由线性求解器优化，比常用的非线性求解器效率更高。”这项研究的财政支持来自国家研究基金会Sing Apore。我们的新闻记者引用了南洋理工大学的一篇研究文章：“二，CT-LIO从正确的关联中获得的好处大于迭代次数，基于这些思想，我们用定制的求解器实现了我们的方法，在每一个增量步骤之后立即执行特征关联过程。”我们的实现可以在高密度控制点的情况下实现实时性能，同时在高动态运动场景中产生竞争性能。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Robotic s - Robotics and Automation. According to news reporting out of Singapore, Singa pore, by NewsRx editors, research stated, “In this letter, we propose a continuo us-time lidar-inertial odometry (CT-LIO) system named SLICT2, which promotes two main insights. One, contrary to conventional wisdom, CT-LIO algorithm can be op timized by linear solvers in only a few iterations, which is more efficient than commonly used nonlinear solvers.” Financial support for this research came from National Research Foundation, Sing apore. Our news journalists obtained a quote from the research from Nanyang Technologic al University, “Two, CT-LIO benefits more from the correct association than the number of iterations. Based on these ideas, we implement our method with a custo mized solver where the feature association process is performed immediately afte r each incremental step, and the solution can converge within a few iterations. Our implementation can achieve real-time performance with a high density of cont rol points while yielding competitive performance in highly dynamical motion sce narios.”
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