PG-LIO: 光度-几何融合的鲁棒激光雷达-惯性里程计
PG-LIO: Photometric-Geometric fusion for Robust LiDAR-Inertial Odometry
AI总结：
PG-LIO通过融合激光雷达光度与几何信息及IMU惯性约束，在退化场景中显著提升LIO精度，并在自相似隧道中实现1公里仅1米漂移。
摘要：
激光雷达-惯性里程计（LIO）被广泛用于精确的状态估计和建图，这是自主机器人的基本需求。传统的LIO方法通常依赖于从激光雷达采样的几何结构来构建约束。因此，在缺乏几何结构的情况下，这些方法往往会变得病态（退化）并失败。LIO对此类条件的鲁棒性是其更广泛部署的必要条件。为了解决这个问题，我们提出了PG-LIO，一种实时的LIO方法，它融合了激光雷达采样的光度信息和几何信息，以及来自惯性测量单元（IMU）的惯性约束。这种多模态信息被集成到一个在滑动窗口上优化的因子图中，以实现实时操作。我们在多个数据集上评估了PG-LIO，这些数据集包括几何条件良好以及自相似场景。我们的方法在几何结构良好的环境中达到了与最先进的LIO相当的精度，同时在退化情况下显著提高了精度，包括与也融合强度的方法相比。值得注意的是，我们展示了在平均速度7.5米/秒（最大速度10.8米/秒）下，通过几何自相似隧道的手动驾驶空中轨迹中，仅漂移1米。为了社区的利益，我们还将发布我们的源代码https://github.com/ntnu-arl/mimosa。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
