路端多源数据空间一致性数据集构建及评估方法研究

Construction of Roadside Multi-source Data Space Consistency Dataset and Research on Evaluation Methods

构建多源传感数据空间一致性是路端多模态数据融合的基础,在车路协同及路端智能中发挥重要作用.然而,现有的路端多模态数据集主要侧重于目标检测等识别类任务的研究,缺少多源传感器之间的多种空间变换信息,不足以支撑路端多源数据空间一致性问题的研究.因此,文中构建一个专门用于路端多源数据空间一致性问题研究的数据集——InfraCalib(https://github.com/chenzhiwei888/InfraCalib-Dataset).数据集共包含23万多帧图像与点云数据,由两个路端智能移动设备采集,覆盖场景、模态、光照、设备空间位置及传感器姿态等多样变化.通过匹配特征关键点对关联多模态数据,构建PnP(Perspective-n-Point)问题,并利用最小重投影误差法解算外参矩阵,作为近似真值标签.最后,在InfraCalib数据集上进行经典特征匹配算法的实验分析,并讨论多源传感器外参标定的量化评估指标.

The spatial consistency of multi-source sensing data is established as the foundation for the fusion of roadside multi-modal data,playing a crucial role in vehicle-to-infrastructure and roadside intelligence.However,existing roadside multi-modal datasets predominantly focus on recognition tasks such as object detection,lacking various spatial transformation information between multi-source sensors.This deficiency hinders the research into the spatial consistency problem of multi-source data at the roadside.Therefore,a dataset specifically designed for the study of the spatial consistency problem in roadside multi-source data is constructed in this paper-InfraCalib(https://github.com/chenzhiwei888/InfraCalib-Dataset).The dataset comprises over 230,000 frames of images and point cloud data,collected by two roadside smart mobile devices,covering diverse changes in scenes,modalities,lighting,device spatial positions and sensor postures.By matching feature key point pairs to correlate multi-modal data,a perspective-n-point(PnP)problem is constructed,and the extrinsic parameter matrix is solved using the minimum reprojection error method,serving as an approximate ground truth label.Finally,an experimental analysis of the classic feature matching algorithm is conducted on the InfraCalib dataset,and the discussion revolves around the quantitative evaluation indicators for the calibration of external parameters in multi-source sensors.