用李群建模运动，提升激光惯性里程计在稀疏环境下的稳定性。
LIMOncello: Iterated Error-State Kalman Filter on the SGal(3) Manifold for Fast LiDAR-Inertial Odometry
- 在SGal(3)流形上构建迭代误差状态卡尔曼滤波器，避免传统方法的数值漂移。
- 相比kd树结构，i-Octree地图更新更快、内存降低60%以上。
- 适合高实时性要求的自动驾驶与机器人导航系统使用。
本文提出LIMOncello，一种紧耦合激光惯性里程计系统，将6自由度运动建模于SGal(3)流形上，并采用迭代误差状态卡尔曼滤波器进行后端优化。相较于SO(3)×R⁶表示方式，该方法在离散时间传播中具有更好的一致性和数值稳定性，有效抑制低可观测条件下的漂移。系统还引入轻量级增量式i-Octree映射后端，实现更快的更新速度和显著更低的内存占用，且无需依赖局部性搜索启发式策略。在多个真实数据集上的实验表明，该系统在几何稀疏环境中仍保持竞争性精度与鲁棒性，同时具备实时性能和稳定的内存增长。代码已开源，地址为https://github.com/CPerezRuiz335/LIMOncello。
原文摘要 · Abstract (English)
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.
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