Fast bi-monocular Visual Odometry using Factor Graph Sparsification

Visual navigation has become a standard in robotic applications with the emergence of robust and versatile algorithms. In particular, Visual Odometry (VO) has proven to be the most reliable navigation solution for space missions to estimate an unmanned vehicle's motion and state. Lava Tubes exploration is one of the recent challenges in this field of applied robotics. VO in this scenario requires more robustness to poor lighting conditions while keeping a low computational cost. We propose investigating an indirect bi-monocular VO based on sliding-window optimization in such a context. It focuses on maintaining the sparsity of the problem while keeping the information of the marginalized frames to reduce the computational burden. Different sparse graph topologies are studied to encode information from the past and are evaluated on accuracy and computation load. The best method retained is then compared to state-of-the-art systems on real data under extreme illumination conditions and reaches similar accuracy results at a lower computational cost.