COIN-LIO：互补强度增强的激光雷达惯性里程计
COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry
AI总结：
针对几何退化场景下激光雷达惯性里程计鲁棒性不足的问题，提出COIN-LIO方法，融合强度图像光度信息与点云配准、惯性测量，提升了精度与鲁棒性，并发布了新的几何退化场景数据集。
摘要：
我们提出了COIN-LIO，这是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水线。我们的工作重点是提升激光雷达惯性里程计在隧道、平坦场地等几何退化场景下的鲁棒性。我们将激光雷达强度回波投影为强度图像，并提出了一套图像处理流水线，可生成滤波后的图像，提升图像内部以及不同场景间的亮度一致性。为有效利用强度这一额外模态，我们提出了一种新颖的特征选择方案，该方案可检测点云配准中的无信息方向，并明确选择具有互补图像信息的图像块。随后，图像块中的光度误差最小化与惯性测量、点到面配准在迭代扩展卡尔曼滤波器中融合。所提方法在一个公开数据集上提升了精度与鲁棒性。我们还发布了一个新数据集，涵盖5个真实环境中的具有挑战性的几何退化场景。通过使用额外的光度信息，我们的方法在所有对比基线方法均失效的环境中，针对几何退化的鲁棒性得到了大幅提升。
英文摘要：
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.
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