IPR-VINS: Real-time monocular visual-inertial SLAM with implicit plane optimization

Abstract

SLAM typically involves updating numerous landmarks to reconstruct scenes rather than generating a topological depiction of the actual environment. Planar features, prevalent in structured environments, can contribute to the development of more nuanced yet less cumbersome maps. The incorporation of fusions between feature points and planar facets gives rise to new discrepancies that must be meticulously addressed. To prevent the uncontrolled expansion of planar boundaries, a robust technique for boundary extraction is offered. Scene planes are fused using the boundary function, ensuring stable feature extraction and planar association. To leverage limited computational resources effectively, a streamlined back-end framework is constructed. Finally, factors are fine-tuned at a consistent scale, which not only elevates locational accuracy but also diminishes computational complexity. To reduce systematic errors, plane prior information is assimilated into global optimization. Compared with VINS-Mono, the average Absolute Trajectory Error of the proposed method on EuRoC is reduced by 44%.