State-of-the-art recursive sensor filtering frameworks allow the fusion of multiple sensors tailored to a specific problem but do not allow a dynamic and efficient introduction of additional sensors during runtime - an important feature to enable long-term missions in dynamic environments. This letter presents a robust, modular sensor-fusion framework that enables the addition and removal of sensors at runtime.These sensors could not be a priori known to the system. The framework handles the complexity of system and sensor initialization, measurement updates, and switching of asynchronous multi-rate sensor information with sensor self-calibration in a truly modular and generic design. In addition, the framework can handle delayed measurements, out-of-sequence updates, and can monitor sensor health. The introduced true-modularity is based on covariance segmentation to allow the isolated (i.e., modular) processing of propagation and updates on a per-sensor basis. We show how crucial properties of the overall state covariance can be maintained as naive implementation of such a modularization would invalidate the covariance matrix. We evaluate our framework for a precision landing scenario switching between combinations of GNSS, barometer, and vision measurements. Tests are performed in simulation and in real-world scenarios to show the validity of the introduced method. The presented framework will be open-sourced and made available online to the community.

中文翻译：

---

MaRS：模块化且强大的传感器融合框架

最先进的递归传感器过滤框架允许融合针对特定问题定制的多个传感器，但不允许在运行时动态有效地引入额外的传感器——这是在动态环境中实现长期任务的重要功能。这封信提出了一个强大的模块化传感器融合框架，可以在运行时添加和移除传感器。这些传感器不可能是系统先验已知的。该框架以真正的模块化和通用设计处理系统和传感器初始化、测量更新和异步多速率传感器信息切换以及传感器自校准的复杂性。此外，该框架可以处理延迟测量、无序更新，并可以监控传感器健康状况。引入的真正模块化基于协方差分割，以允许在每个传感器的基础上对传播和更新进行隔离（即模块化）处理。我们展示了如何保持整体状态协方差的关键属性，因为这种模块化的幼稚实现会使协方差矩阵无效。我们评估了我们的框架，用于在 GNSS、气压计和视觉测量组合之间切换的精确着陆场景。测试在模拟和真实场景中进行，以显示所引入方法的有效性。所提出的框架将是开源的，并在线提供给社区。我们展示了如何保持整体状态协方差的关键属性，因为这种模块化的幼稚实现会使协方差矩阵无效。我们评估了我们的框架，用于在 GNSS、气压计和视觉测量组合之间切换的精确着陆场景。测试在模拟和真实场景中进行，以显示所引入方法的有效性。所提出的框架将是开源的，并在线提供给社区。我们展示了如何保持整体状态协方差的关键属性，因为这种模块化的幼稚实现会使协方差矩阵无效。我们评估了我们的框架，用于在 GNSS、气压计和视觉测量组合之间切换的精确着陆场景。测试在模拟和真实场景中进行，以显示所引入方法的有效性。所提出的框架将是开源的，并在线提供给社区。