Differential Geometric SLAM
This work addresses SLAM for robotics or autonomous systems, but it appears incremental as it builds on existing methods with a focus on stability proofs.
The paper tackled the 3D simultaneous localization and mapping (SLAM) problem by proposing DG-SLAM, a differential geometry-based algorithm that is provably asymptotically stable under ideal conditions, with simulations showing successful localization and mapping in noisy environments.
The simultaneous localization and mapping (SLAM) problem is considered in three dimensions. The proposed algorithm, differential geometric SLAM (DG-SLAM), employs methods from differential geometry to propagate the state and map estimates. Unlike EKF SLAM, the proposed filter is provably asymptotically stable under the assumption of no measurement noise or biases. The robustness of the DG-SLAM algorithm is assessed in simulation with measurement noise. The simulation demonstrates successful localization and mapping.