Stratified Particle Filter Monocular SLAM

Stratified Particle Filter Monocular SLAM

Blog Article

This paper presents a solution to the problem of simultaneous localization and mapping (SLAM), developed from a particle filter, utilizing a monocular camera as its main sensor.It implements a novel sample-weighting idea, based on the of sorting of particles into sets and separating those sets with an importance-factor offset.The grouping criteria for samples is the number of landmarks correctly matched by a given 9002nc particle.

This results in the stratification of samples and amplifies weighted differences.The proposed system is designed for a UAV, navigating outdoors, with a downward-pointed camera.To evaluate the proposed method, it is compared with different samples-weighting approaches, using simulated and real-world data.

The conducted strikketøy oppbevaring experiments show that the developed SLAM solution is more accurate and robust than other particle-filter methods, as it allows the employment of a smaller number of particles, lowering the overall computational complexity.