Semantic and fiducial-aided graph simultaneous localization and mapping (SF-GraphSLAM) for robotic in-space assembly...

This article proposes a method that uses information about modules and desired assembly locations within a large truss structure to create a semantic and fiducial aided graph simultaneous localization and mapping (SF-GraphSLAM) algorithm that is better tailored for use during robotic in-space assembly and servicing operations. This is achieved by first reducing the number of modules using a mixed assembly method vs. a strut-by-strut method. Then, each module is correlated to a visual tag (in this article, an AprilTag) to reduce the number of elements being observed further from the number of sub-struts in that module to a single AprilTag marker. Two tags are required to ensure proper deployment of most deployable modules. Subsequently, we are able to use semantic information about the desired transformation matrix between any two adjacent module AprilTags within the desired assembly structure. For our experimentation, we expanded a factor graph smoothing and mapping model and added the semantic information, looking at the smaller number of landmark AprilTags, with a camera representing the robot for simplicity. The mathematical approach to arrive at this new method is included in this article, as are simulations to test it against the state of the art (SOA) using no structural knowledge. Overall, this research contributes to the SOA for both general SLAM work and, more specifically, to the underdeveloped field of SLAM for in-space assembly and servicing of large truss structures. It is critical to ensure that as a robot is assembling the modules, each module is within the desired tolerances to ensure the final structure is within the design requirements. Being able to build a virtual twin of the truss structure as it is being assembled is a key tent pole in achieving large space structures.