Outlier Robustness in Server-Assisted Collaborative SLAM : Evaluating Outlier Impact and Improving Robustness

Abstract: In order to be able to perform many tasks, autonomous devices need to understand their environment and know where they are in this environment. Simultaneous Localisation and Mapping (SLAM) is a solution to this problem. When several devices attempt to jointly solve this problem they use Collaborative SLAM (C-SLAM), but this is a very resource-demanding process. In order to enable resource-constrained devices, like small mobile robots or eXtended Reality (XR) devices, to run C-SLAM we look towards a Server-Assisted C-SLAM architecture to lift the computational burden from these devices. In a real-world scenario, sensors might fail, the devices might process sensor data wrongly or a malicious actor might inject wrong data into the system. In order for these solutions to be reliable, they must be able to deal with these \emph{outliers}. This thesis looks into the impact of outliers in Server-Assisted C-SLAM algorithms and presents two novel solutions for a robust algorithm, based on robust estimation of the initial device poses. We show the novel solutions outperform the state of the art both in estimation accuracy, yielding better estimates of the real device trajectories, and computational performance, making it suitable for device-constrained devices.