Aggressive motions from agile flights or traversing irregular terrain induce motion distortion in LiDAR scans that can degrade state estimation and mapping. Some methods exist to mitigate this effect, but they are still too simplistic or computationally costly for resource-constrained mobile robots. To this end, this paper presents Direct LiDAR-Inertial Odometry (DLIO), a lightweight LiDAR-inertial odometry algorithm with a new coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction. The key to our method lies in the construction of a set of analytical equations which are parameterized solely by time, enabling fast and parallelizable point-wise deskewing. This method is feasible only because of the strong convergence properties in our nonlinear geometric observer, which provides provably correct state estimates for initializing the sensitive IMU integration step. Moreover, by simultaneously performing motion correction and prior generation, and by directly registering each scan to the map and bypassing scan-to-scan, DLIO's condensed architecture is nearly 20% more computationally efficient than the current state-of-the-art with a 12% increase in accuracy. We demonstrate DLIO's superior localization accuracy, map quality, and lower computational overhead as compared to four state-of-the-art algorithms through extensive tests using multiple public benchmark and self-collected datasets.

翻译：敏捷飞行或穿越不规则地形产生的剧烈运动会引起激光雷达扫描中的运动畸变，从而降低状态估计与建图精度。现有一些方法虽能减轻该影响，但对资源受限的移动机器人而言，其处理方式仍过于简单或计算成本过高。为此，本文提出直接激光雷达-惯性里程计（DLIO）——一种轻量级激光雷达-惯性里程计算法，其采用新的粗到细策略构建连续时间轨迹以实现精确运动补偿。该方法的核心在于构建一组仅以时间为参数的解析方程，从而支持快速可并行的逐点畸变校正。该方法得以实现的关键在于非线性几何观测器强大的收敛特性，该观测器能为敏感的IMU积分初始化步骤提供可证明正确的状态估计。此外，通过同步执行运动补偿与先验生成，并直接实现逐帧点云与地图的配准（跳过扫描间配准步骤），DLIO的紧凑架构相比现有最优算法在计算效率上提升近20%，精度提高12%。通过使用多个公开基准数据集与自采集数据集进行广泛测试，我们证明了DLIO相比四种现有最优算法具有更优的定位精度、建图质量以及更低计算开销。