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：我们提出COIN-LIO，一种将激光雷达强度信息与基于几何的点云配准紧密耦合的激光雷达惯性里程计流程。本工作的重点是提高激光雷达惯性里程计在几何退化场景（如隧道或平坦区域）中的鲁棒性。我们将激光雷达强度回波投影至强度图像，并提出一种图像处理流程，该流程可生成在图像内部及跨场景均具有更优亮度一致性的滤波图像。为有效利用强度作为附加模态，我们提出一种新颖的特征选择方案，该方案可检测点云配准中的无效方向，并显式选取具有互补图像信息的图像块。随后，在迭代扩展卡尔曼滤波器中，将图像块的光度误差最小化与惯性测量及点面配准相融合。所提方法在公开数据集上提升了精度与鲁棒性。此外，我们发布了一个新数据集，该数据集在具有挑战性的几何退化场景中捕获了五种真实环境。通过利用额外的光度信息，我们的方法在所有对比基线方法均失效的环境中，展现出显著提升的对几何退化的鲁棒性。