AS-LIO：面向剧烈FOV变化的空间重叠度引导自适应滑动窗口LiDAR-惯性里程计
AS-LIO: Spatial Overlap Guided Adaptive Sliding Window LiDAR-Inertial Odometry for Aggressive FOV Variation
AI总结：
针对剧烈运动下FOV变化导致的非线性误差和约束退化问题，提出由空间重叠度引导的自适应滑动窗口LIO框架（AS-LIO），动态调整更新频率以精确表征轨迹，显著提升了定位精度与鲁棒性。
摘要：
LiDAR-Inertial Odometry (LIO，激光雷达-惯性里程计) 在一般低速和平稳运动场景中展现出卓越的精度与稳定性。然而，在高速和剧烈运动场景（如急转弯）中，会出现两个主要挑战：首先，由于IMU频率的限制，估计显著非线性运动状态的误差会急剧上升；其次，Field of View (FOV，视场角) 的剧烈变化可能会减少LiDAR帧与点云地图（或帧与帧之间）的空间重叠度，导致数据关联不足和约束退化。为了解决这些问题，我们提出了一种由Spatial Overlap Degree (SOD，空间重叠度) 引导的新型Adaptive Sliding window LIO framework (AS-LIO，自适应滑动窗口LIO框架)。首先，我们评估LiDAR帧与已配准地图之间的SOD，直接评估当前FOV变化对点云配准的不利影响。随后，我们设计了一个自适应滑动窗口来管理连续的LiDAR数据流并控制状态更新，根据SOD动态调整更新步长。这一策略使我们的里程计能够自适应地采用更高的更新频率，在剧烈FOV变化期间精确表征轨迹，从而有效降低定位中的非线性误差。同时，滑动窗口内的历史约束增强了帧到地图的数据关联，确保了状态估计的鲁棒性。实验表明，我们的AS-LIO框架能够快速感知并响应具有挑战性的FOV变化，在精度和鲁棒性方面优于其他state-of-the-art LIO框架。
英文摘要：
LiDAR-Inertial Odometry (LIO) demonstrates outstanding accuracy and stability in general low-speed and smooth motion scenarios. However, in high-speed and intense motion scenarios, such as sharp turns, two primary challenges arise: firstly, due to the limitations of IMU frequency, the error in estimating significantly non-linear motion states escalates; secondly, drastic changes in the Field of View (FOV) may diminish the spatial overlap between LiDAR frame and pointcloud map (or between frames), leading to insufficient data association and constraint degradation. To address these issues, we propose a novel Adaptive Sliding window LIO framework (AS-LIO) guided by the Spatial Overlap Degree (SOD). Initially, we assess the SOD between the LiDAR frames and the registered map, directly evaluating the adverse impact of current FOV variation on pointcloud alignment. Subsequently, we design an adaptive sliding window to manage the continuous LiDAR stream and control state updates, dynamically adjusting the update step according to the SOD. This strategy enables our odometry to adaptively adopt higher update frequency to precisely characterize trajectory during aggressive FOV variation, thus effectively reducing the non-linear error in positioning. Meanwhile, the historical constraints within the sliding window reinforce the frame-to-map data association, ensuring the robustness of state estimation. Experiments show that our AS-LIO framework can quickly perceive and respond to challenging FOV change, outperforming other state-of-the-art LIO frameworks in terms of accuracy and robustness.
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。
