We present COIN-LIO, a LiDAR Inertial Odometry pipeline that tightly couples information from LiDAR intensity with geometry-based point cloud registration. The focus of our work is to improve the robustness of LiDAR-inertial odometry in geometrically degenerate scenarios, like tunnels or flat fields. We project LiDAR intensity returns into an intensity image, and propose an image processing pipeline that produces filtered images with improved brightness consistency within the image as well as across different scenes. To effectively leverage intensity as an additional modality, we present a novel feature selection scheme that detects uninformative directions in the point cloud registration and explicitly selects patches with complementary image information. Photometric error minimization in the image patches is then fused with inertial measurements and point-to-plane registration in an iterated Extended Kalman Filter. The proposed approach improves accuracy and robustness on a public dataset. We additionally publish a new dataset, that captures five real-world environments in challenging, geometrically degenerate scenes. By using the additional photometric information, our approach shows drastically improved robustness against geometric degeneracy in environments where all compared baseline approaches fail.

翻译：摘要：本文提出COIN-LIO，一种紧密耦合激光雷达强度信息与基于几何的点云配准的激光雷达惯性里程计方法。本工作旨在提升激光雷达惯性里程计在几何退化场景（如隧道或平旷区域）中的鲁棒性。我们将激光雷达强度回波投影至强度图像，并提出图像处理流程：通过生成滤波图像改善图像内亮度一致性及跨场景亮度一致性。为有效利用强度这一额外模态，我们提出新颖的特征选择方案：检测点云配准中的非信息性方向，并显式选择具有互补图像信息的图像块。在迭代扩展卡尔曼滤波框架中，图像块光度误差最小化与惯性测量及点到平面配准相融合。该方法在公开数据集上提升了精度与鲁棒性。此外，我们发布了新数据集，该数据集包含五个真实环境中的挑战性几何退化场景。通过利用额外光度信息，本方法在所有对比基线方法均失效的环境中，展现出对几何退化的显著增强鲁棒性。