融合光照与几何信息，提升激光惯性里程计在无结构环境下的稳定性。
PG-LIO: Photometric-Geometric fusion for Robust LiDAR-Inertial Odometry
- 利用激光雷达的光照与几何信息，结合惯性数据构建优化因子图。
- 在自相似隧道中1公里轨迹仅1米漂移，速度达7.5米/秒。
- 适合复杂或缺乏几何特征场景的机器人定位，开源代码已发布。
激光惯性里程计（LIO）广泛用于自主机器人中的精准状态估计与建图。传统LIO方法主要依赖激光雷达采样到的几何结构，因此在缺乏几何结构时易出现病态（退化）并失效。为提升鲁棒性，我们提出PG-LIO，一种实时运行的多模态融合方法，将激光雷达采集的光照信息与几何信息、以及惯性测量单元（IMU）的惯性约束共同融入滑动窗口优化的因子图中。我们在多个数据集上评估了该方法，涵盖几何结构良好和自相似场景。结果表明，在几何结构良好的条件下，PG-LIO精度与当前最优方法相当；而在退化场景下，其性能显著优于其他融合强度信息的方法。特别地，在人工驾驶的空中飞行轨迹中，以平均7.5米/秒（最高速度10.8米/秒）穿越几何自相似隧道，1公里行程仅产生1米漂移。相关源码将开源发布于https://github.com/ntnu-arl/mimosa。
原文摘要 · Abstract (English)
LiDAR-Inertial Odometry (LIO) is widely used for accurate state estimation and mapping which is an essential requirement for autonomous robots. Conventional LIO methods typically rely on formulating constraints from the geometric structure sampled by the LiDAR. Hence, in the lack of geometric structure, these tend to become ill-conditioned (degenerate) and fail. Robustness of LIO to such conditions is a necessity for its broader deployment. To address this, we propose PG-LIO, a real-time LIO method that fuses photometric and geometric information sampled by the LiDAR along with inertial constraints from an Inertial Measurement Unit (IMU). This multi-modal information is integrated into a factor graph optimized over a sliding window for real-time operation. We evaluate PG-LIO on multiple datasets that include both geometrically well-conditioned as well as self-similar scenarios. Our method achieves accuracy on par with state-of-the-art LIO in geometrically well-structured settings while significantly improving accuracy in degenerate cases including against methods that also fuse intensity. Notably, we demonstrate only 1 m drift over a 1 km manually piloted aerial trajectory through a geometrically self-similar tunnel at an average speed of 7.5m/s (max speed 10.8 m/s). For the benefit of the community, we shall also release our source code https://github.com/ntnu-arl/mimosa
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