Existing LiDAR-Inertial Odometry (LIO) methods typically utilize the prior trajectory derived from the IMU integration to compensate for the motion distortion within LiDAR frames. However, discrepancies between the prior and true trajectory can lead to residual motion distortions that compromise the consistency of LiDAR frame with its corresponding geometric environment. This imbalance may result in pointcloud registration becoming trapped in local optima, thereby exacerbating drift during long-term and large-scale localization. To this end, we propose a novel LIO framework with selective intra-frame smoothing dubbed AC-LIO. Our core idea is to asymptotically backpropagate current update term and compensate for residual motion distortion under the guidance of convergence criteria, aiming to improve the accuracy of discrete-state LIO system with minimal computational increase. Extensive experiments demonstrate that our AC-LIO framework further enhances odometry accuracy compared to prior arts, with about 30.4% reduction in average RMSE over the second best result, leading to marked improvements in the accuracy of long-term and large-scale localization and mapping.

翻译：现有激光雷达-惯性里程计（LIO）方法通常利用来自惯性测量单元（IMU）积分的先验轨迹来补偿激光雷达帧内的运动畸变。然而，先验轨迹与真实轨迹之间的偏差会导致残余运动畸变，破坏LiDAR帧与其对应几何环境的一致性。这种不平衡可能导致点云配准陷入局部最优，从而加剧长期大规模定位中的漂移问题。为此，我们提出一种新颖的LIO框架——AC-LIO，其采用选择性帧内平滑策略。核心思想是在收敛准则引导下渐近反向传播当前更新项并补偿残余运动畸变，旨在以极小的计算增量提升离散状态LIO系统的精度。大量实验表明，与现有最优方法相比，AC-LIO框架进一步提升了里程计精度，其平均RMSE相较于次优结果降低约30.4%，显著改善了长期大规模定位与建图的准确性。