LiDAR-惯性里程计在动态驾驶场景中基于标签一致性检测的方法
LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection
AI总结：
本文提出一种基于标签一致性检测的LiDAR-惯性里程计方法，通过二值化标签差异识别并移除动态物体，在六个公开数据集上验证了低计算开销（1-9毫秒/扫描）和先进的位姿估计精度。
摘要：
本文提出了一种LiDAR-惯性里程计（LIO）方法，旨在消除动态驾驶场景中移动物体对定位的影响。该方法为当前扫描的3D点构建二值化标签，并利用每个点与地图中周围点的标签差异来识别移动物体。首先，通过地面分割为当前扫描中的每个3D点分配二值化标签，即地面点与非地面点。在实际驾驶场景中，动态物体通常位于地面上。对于从移动物体扫描到的大多数点，它们无法与空间中的任何现有结构重合。对于少数靠近地面的移动物体点，其标签与周围地面点存在差异。因此，移动物体上的点因在地图中缺乏最近邻点或与周围地面点标签不一致而被识别。通过基于体素位置的最近邻搜索定位全局地图中的最近邻点，并通过比较与最近邻点的标签一致性来评估一致性，无需大量计算。最后，移除移动物体上的点。所提方法嵌入到自主研发的LIO系统（即Dynamic-LIO）中，在六个公开数据集上进行了评估，并在动态和静态环境中进行了测试。实验结果表明，该方法能够以极低的计算开销（即1-9毫秒/扫描）识别移动物体，且Dynamic-LIO在静态和动态场景中均能达到最先进的位姿估计精度。我们已发布该工作的源代码，以促进社区的发展。
英文摘要：
In this paper, a LiDAR-inertial odometry (LIO) method that eliminates the influence of moving objects in dynamic driving scenarios is proposed. This method constructs binarized labels for 3D points of current sweep, and utilizes the label difference between each point and its surrounding points in map to identify moving objects. Firstly, the binarized labels, i.e., ground and non-ground are assigned to each 3D point in current sweep using ground segmentation. In actual driving scenarios, dynamic objects are always located on the ground. For most points scanned from moving objects, they cannot coincide with any existing structures in space. For a minority of moving objects' points that are close to the ground, their labels exhibit differences with surrounding ground points. Thus, the points on moving objects are identified due to lacking of nearest neighbors in map or inconsistency with the labels of surround ground points. The nearest neighbors from global map are localized by voxel-location-based nearest neighbor search and the consistency is evaluated by comparing the label consistency with nearest neighbors, without involving any massive computations. Finally, the points on moving objects are removed. The proposed method is embeded into a self-developed LIO system (i.e., Dynamic-LIO), evaluated with six public datasets, and tested in both dynamic and static environments. Experimental results demonstrate that our method can identify moving objects with extremlely low computational overhead (i.e., 1-9ms/sweep), and our Dynamic-LIO can achieve state-of-the-art pose estimation accuracy in both static and dynamic scenarios. We have released the source code of this work for the development of the community.
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。
