This work introduces LIMOncello, a tightly coupled LiDAR-Inertial Odometry system that models 6-DoF motion on the $\mathrm{SGal}(3)$ manifold within an iterated error-state Kalman filter backend. Compared to state representations defined on $\mathrm{SO}(3)\times\mathbb{R}^6$, the use of $\mathrm{SGal}(3)$ provides a coherent and numerically stable discrete-time propagation model that helps limit drift in low-observability conditions. LIMOncello also includes a lightweight incremental i-Octree mapping backend that enables faster updates and substantially lower memory usage than incremental kd-tree style map structures, without relying on locality-restricted search heuristics. Experiments on multiple real-world datasets show that LIMOncello achieves competitive accuracy while improving robustness in geometrically sparse environments. The system maintains real-time performance with stable memory growth and is released as an extensible open-source implementation at https://github.com/CPerezRuiz335/LIMOncello.

翻译：本文提出了 LIMOncello，一种紧耦合的激光雷达-惯性里程计系统。该系统在迭代误差状态卡尔曼滤波器后端中，将六自由度运动建模于 $\mathrm{SGal}(3)$ 流形上。与定义在 $\mathrm{SO}(3)\times\mathbb{R}^6$ 上的状态表示相比，使用 $\mathrm{SGal}(3)$ 提供了一个一致且数值稳定的离散时间传播模型，有助于在低可观测性条件下限制漂移。LIMOncello 还包含一个轻量级的增量 i-Octree 建图后端，该后端无需依赖局部受限的搜索启发式方法，即可实现比增量 kd-树式地图结构更快的更新速度和显著更低的内存占用。在多个真实世界数据集上的实验表明，LIMOncello 在保持具有竞争力的精度的同时，提高了在几何稀疏环境中的鲁棒性。该系统保持了实时性能与稳定的内存增长，并已在 https://github.com/CPerezRuiz335/LIMOncello 上发布为可扩展的开源实现。