We propose a Simultaneous Localization and Mapping (SLAM)‐based Integrity Monitoring (IM) algorithm using GPS and fish‐eye camera to compute the protection levels while accounting for multiple faults in GPS and vision. We perform graph optimization using GPS pseudoranges, pixel intensities, vehicle dynamics, and satellite ephemeris to simultaneously localize the vehicle, GPS satellites, and key image pixels in the world frame. We estimate the fault mode vector by analyzing the temporal correlation across pseudorange residuals and spatial correlation across pixel intensity residuals. To isolate the vision faults, we develop a superpixel‐based piecewise random sample consensus. For the estimated fault mode, we compute the protection levels by performing worst‐case failure slope analysis on the batch realization of linearized Graph‐SLAM formulation. We perform real‐world experiments in an alleyway in Stanford, California and a semi‐urban area in Champaign, Illinois. We demonstrate higher localization accuracy and tighter protection levels as compared to GPS‐only SLAM‐based IM.

中文翻译：

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使用GPS和鱼眼镜头对Graph‐SLAM进行完整性监控

我们提出了一种基于同时定位和映射（SLAM）的完整性监控（IM）算法，该算法使用GPS和鱼眼摄像头来计算保护级别，同时考虑到GPS和视觉中的多个故障。我们使用GPS伪距，像素强度，车辆动力学和卫星星历来执行图形优化，以同时定位世界框架中的车辆，GPS卫星和关键图像像素。我们通过分析伪距残差之间的时间相关性和像素强度残差之间的空间相关性来估计故障模式向量。为了隔离视觉缺陷，我们开发了基于超像素的分段随机样本共识。对于估计的故障模式，我们通过对线性化Graph-SLAM公式的批量实现执行最坏情况的故障斜率分析来计算保护级别。我们在加利福尼亚州斯坦福市的一条小巷和伊利诺伊州尚佩恩的一个半城市地区进行现实世界的实验。与仅基于GPS的SLAM即时消息相比，我们证明了更高的定位精度和更严格的保护级别。