arXiv 2502.192422025-07-17cs.RO

BEV-LIO(LC)：基于鸟瞰图辅助并带有回环检测的LiDAR-惯性里程计

BEV-LIO(LC): BEV Image Assisted LiDAR-Inertial Odometry with Loop Closure

School of Computer Science and Technology, Guangdong University of Technology（计算机科学与技术学院，广东技术大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电子与电气工程学院，南洋理工大学）

Haoxin Cai, Shenghai Yuan, Xinyi Li, Junfeng Guo, Jianqi Liu

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AI总结：

本文提出BEV-LIO(LC)框架，将LiDAR点云投影为BEV图像并结合CNN特征提取、iEKF配准与因子图优化，利用全局描述子实现回环检测，在多种场景下取得优于现有方法的定位精度。

摘要：

本文提出了BEV-LIO(LC)，一种新颖的LiDAR-惯性里程计（LIO）框架，该框架将LiDAR数据的鸟瞰图（BEV）图像表示与基于几何的点云配准相结合，并通过BEV图像特征引入回环检测（LC）。通过对点密度进行归一化，我们将LiDAR点云投影为BEV图像，从而实现高效的特征提取与匹配。采用一个轻量级卷积神经网络（CNN）特征提取器从BEV图像中提取具有区分性的局部和全局描述子。局部描述子用于将BEV图像与FAST关键点进行匹配以构建重投影误差，而全局描述子则用于辅助回环检测。随后，重投影误差最小化与点到平面配准在迭代扩展卡尔曼滤波器（iEKF）中进行融合。在后端，利用全局描述子构建基于KD-tree索引的关键帧数据库，以实现准确的回环检测。当检测到回环时，随机采样一致性（RANSAC）根据BEV图像匹配计算粗略变换，并将其作为迭代最近点（ICP）的初始估计。精化后的变换随后与里程计因子一起被纳入因子图中，从而提升定位的全局一致性。在不同场景和不同LiDAR类型上进行的大量实验表明，BEV-LIO(LC)优于现有最先进方法，取得了具有竞争力的定位精度。我们的代码和视频可在https://github.com/HxCa1/BEV-LIO-LC获取。

英文摘要：

This work introduces BEV-LIO(LC), a novel LiDAR-Inertial Odometry (LIO) framework that combines Bird's Eye View (BEV) image representations of LiDAR data with geometry-based point cloud registration and incorporates loop closure (LC) through BEV image features. By normalizing point density, we project LiDAR point clouds into BEV images, thereby enabling efficient feature extraction and matching. A lightweight convolutional neural network (CNN) based feature extractor is employed to extract distinctive local and global descriptors from the BEV images. Local descriptors are used to match BEV images with FAST keypoints for reprojection error construction, while global descriptors facilitate loop closure detection. Reprojection error minimization is then integrated with point-to-plane registration within an iterated Extended Kalman Filter (iEKF). In the back-end, global descriptors are used to create a KD-tree-indexed keyframe database for accurate loop closure detection. When a loop closure is detected, Random Sample Consensus (RANSAC) computes a coarse transform from BEV image matching, which serves as the initial estimate for Iterative Closest Point (ICP). The refined transform is subsequently incorporated into a factor graph along with odometry factors, improving the global consistency of localization. Extensive experiments conducted in various scenarios with different LiDAR types demonstrate that BEV-LIO(LC) outperforms state-of-the-art methods, achieving competitive localization accuracy. Our code and video can be found at https://github.com/HxCa1/BEV-LIO-LC.

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