This research focuses on the development of the semantic and fiducial aided graph simultaneous localization and mapping (SF-GraphSLAM) method that is tailored for robotic assembly and servicing of large truss structures. SF-GraphSLAM contributes to the state of the art by creating a novel way to add associations between map landmarks, in this scenario fiducials, by pre-generating a semantic map of expected relations based on the truss module known models, kinematic information about deployable modules, and hardware constraints for assembled modules. This additional information about the expected fiducial relations, and therefore truss module relative poses, can be used to add robustness to camera pose and measurement error. In parallel, the concept of a mixed assembly truss structure paradigm was created and analyzed. This mixed assembly method focuses on reducing the number of modules required to construct large truss structures by using a mixture of deployable and assembled truss modules to create a checkerboard array that is scalable to various dimensions and shapes while still minimizing the number of modules compared to a strut-by-strut method. Leveraging this paradigm SF-GraphSLAM is able to start at an advantage in terms of minimizing the state vector for the assembly testing. In addition, due to the added knowledge of the structure and the choice to utilize fiducial markers, SF-GraphSLAM is able to minimize the number of fiducials used to define the structure and therefore have the minimum state space to solve the assembly scenario, greatly improving the real-time estimation process between assembly steps. These optimizations will have a larger effect as the size of the scaled end structure increases. SF-GraphSLAM is derived in mathematical form following the same core process used for the pose and measurement components used in the base GraphSLAM. SF-GraphSLAM is evaluated against the state of the art example of GraphSLAM through simulation using an example 3x3x3 mixed assembly truss structure, known as the Built On-orbit Robotically-assembled Gigatruss (BORG). A physical BORG test truss was constructed to enable hardware trials of the SF-GraphSLAM algorithm. Although this ground hardware is not ideal for the high precision application of space structures it allows for rapid experimental robotic testing. This tailored SF-GraphSLAM approach will contribute to the state of the art of robotic in-space servicing, assembly, and manufacturing (ISAM) by providing progress on a method for dealing with the autonomous robotic assembly of movable modules to create larger structures. This will be critical for missions such as robotically assembling a large antenna structure or space telescope. Furthermore, the core methodology will study into how to best utilize information in a large-scale structure environment, including non-static flexible or deployable modules, to adequately map it which is also applicable to the larger field of robotic operations dealing with structures such as bridge surveying. / Doctor of Philosophy / The goal of this research is to enable in-space assembly of large truss structures by advancing the state of the art of how a robot can map the structure it is actively assembling. The concept of having a robot create a map of the landmarks, or in this case truss elements, it sees while keeping track of it's own movement is known as simultaneous localization and mapping (SLAM). This research focuses on the creation of a method called semantic and fiducial aided graph simultaneous localization and mapping (SF-GraphSLAM). The added semantic information is the model knowledge of the truss structure the robot is assembling, including what kind of modules are within and their desired relationships to each other. Fiducials are optical markers that can be used to provide identification, position, and orientation of what they are mounted to. Combining these concepts SF-GraphSLAM can use easily identifiable fiducials to mark components of the truss structure and use knowledge of how the truss structure should be assembled to help in estimating where the actual physical components are at different stages of the assembly process. This method is used to check if a truss module is assembled correctly after each step to ensure the final structure is within the requirements desired. This concept can be likened to when assembling a LEGO model, a person verifies they are using the correct brick for the next assembly step and then compared the state of the model with the reference photo before proceeding with the building. An incorrectly assembled module in an early step could result in a module down the line not being able to be properly placed or the final assembled structure not being within operational tolerances. This research shows how SF-GraphSLAM can be implemented for the application of assembling a truss structure out of both deployable and assembled modules. Mathematical analysis, simulations, and hardware testing were completed to compare this new method to the state of the art approach. SF-GraphSLAM is a critical step in the development required to make autonomous robotic assembly of larger structures in space feasible.