In this paper, we propose a highly accurate continuous-time trajectory estimation framework dedicated to SLAM (Simultaneous Localization and Mapping) applications, which enables fuse high-frequency and asynchronous sensor data effectively. We apply the proposed framework in a 3D LiDAR-inertial system for evaluations. The proposed method adopts a non-rigid registration method for continuous-time trajectory estimation and simultaneously removing the motion distortion in LiDAR scans. Additionally, we propose a two-state continuous-time trajectory correction method to efficiently and efficiently tackle the computationally-intractable global optimization problem when loop closure happens. We examine the accuracy of the proposed approach on several publicly available datasets and the data we collected. The experimental results indicate that the proposed method outperforms the discrete-time methods regarding accuracy especially when aggressive motion occurs. Furthermore, we open source our code at \url{https://github.com/APRIL-ZJU/clins} to benefit research community.
翻译:在本文中,我们提出了一个高度准确的连续时间轨迹估计框架,专门用于SLAM(同时定位和绘图)应用,使高频和无同步传感器数据有效结合,我们在3D LiDAR-内皮系统应用拟议框架进行评估,拟议方法采用非硬性登记方法进行连续时间轨迹估计,同时消除LIDAR扫描中的运动扭曲。此外,我们提出一个两州连续时间轨迹修正方法,以便在环圈关闭时高效和高效地处理可计算到的全球优化问题。我们研究了若干可公开获取的数据集和我们收集的数据的拟议方法的准确性。实验结果显示,拟议方法在精确性方面超过了离散时间方法,特别是在发生攻击性运动时。此外,我们还在\url{https://github.com/APRIL-ZJU/clins}打开我们的代码,以惠及研究界。