An Observer Design for Visual Simultaneous Localisation and Mapping with Output Equivariance
This work addresses the need for low-memory, computationally efficient VSLAM algorithms for small embedded robotic systems like aerial vehicles, representing an incremental improvement over existing methods.
The paper tackled the problem of designing a robust and efficient observer for Visual Simultaneous Localisation and Mapping (VSLAM) by exploiting symmetry groups with inverse depth measurements, resulting in a non-linear gradient-based observer that achieved similar accuracy to the Extended Kalman Filter with significant gains in processing time (linear vs. quadratic bounds) and improved robustness.
Visual Simultaneous Localisation and Mapping (VSLAM) is a key enabling technology for small embedded robotic systems such as aerial vehicles. Recent advances in equivariant filter and observer design offer the potential of a new generation of highly robust algorithms with low memory and computation requirements for embedded system applications. This paper studies observer design on the symmetry group proposed in previous work by the authors, in the case where inverse depth measurements are available. Exploiting this symmetry leads to a simple fully non-linear gradient based observer with almost global asymptotic and local exponential stability properties. Simulation experiments verify the observer design, and demonstrate that the proposed observer achieves similar accuracy to the widely used Extended Kalman Filter with significant gains in processing time (linear verses quadratic bounds with respect to number of landmarks) and qualitative improvements in robustness.