Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LRA.2020.3043195 | IEEE Robotics and Automation Letters |
Keywords | DocType | Volume |
Autonomous navigation,modularity,state-estimation,Sensor fusion | Journal | 6 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Brommer | 1 | 3 | 2.21 |
| Roland Jung | 2 | 0 | 1.01 |
| Jan Steinbrener | 3 | 1 | 1.37 |
| Weiss, Stephan | 4 | 209 | 33.25 |