In the last few years, simultaneous localization and mapping became an important topic of research in the robotics community. This article proposes an approach for autonomous navigation of mobile robots in faulty situations. The main objective is to extend the fault tolerance strategy to simultaneous localization and mapping in presence of sensor faults or software faults in the data fusion process. Fault detection and isolation technique is performed based on duplication–comparison method and structured residuals. The proposed fault tolerance approach is based on the extended Kalman filter for simultaneous localization and mapping when an absolute localization sensor is available. The validation of the proposed approach and the extended Kalman filter for simultaneous localization and mapping algorithm is performed from experiments employing an omnidrive mobile robot, equipped with embedded sensors, namely: wheel encoders, gyroscope, two laser rangefinders and external sensor for the absolute position (indoor global positioning system). The obtained results demonstrate the effectiveness of the proposed approach where it was found that its fault tolerance performance is based essentially on the selected residuals and the values of the fault detection thresholds to be used for the fault detection and isolation.

中文翻译：

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面向传感器和软件故障的同步定位和映射：在全向移动机器人中的应用

在过去的几年中，同步定位和映射成为机器人社区的一个重要研究课题。本文提出了一种移动机器人在故障情况下自主导航的方法。主要目标是将容错策略扩展到数据融合过程中存在传感器故障或软件故障的同时定位和映射。故障检测和隔离技术是基于重复比较方法和结构化残差来执行的。当绝对定位传感器可用时，所提出的容错方法基于扩展卡尔曼滤波器，用于同时定位和映射。所提出的方法和用于同时定位和映射算法的扩展卡尔曼滤波器的验证是通过使用配备嵌入式传感器的全方位驱动移动机器人的实验进行的，即：车轮编码器、陀螺仪、两个激光测距仪和用于绝对位置的外部传感器（室内全球定位系统）。获得的结果证明了所提出的方法的有效性，其中发现其容错性能基本上基于所选的残差和用于故障检测和隔离的故障检测阈值的值。