**6. Conclusions**

This article discusses a particle-filter-SLAM algorithm that introduces a novel approach to the particle-weighting procedure.

Theoretical analysis and experimental evaluation were conducted for multiple simulated and real-world flights. As a result, the usage of Mahalanobis gating with weight stratification by the number of matched landmarks, was identified to be a beneficial and desirable element of monocular-particle-filter-SLAM algorithms.

The experiments proved that, overall, performance of a particle filter's simultaneous localization-and-mapping algorithm is better when the presented approach is implemented. The stratified particle filter is more robust and accurate than other filter variants. Furthermore, theloopclosureisperformedmoreeffectivelyandtheparticlesareresampledmoreoften.

 Consequently, the application of the presented algorithm allows to reduce the number of particles—lowering the computational complexity.

**Author Contributions:** Conceptualization, P.S.; methodology, P.S. and P.K.; software, P.S.; validation, P.S. and P.K.; formal analysis, P.S. and P.K.; investigation, P.S. and P.K.; resources, P.S.; data curation, P.S.; writing—original draft preparation, P.S.; writing—review and editing, P.K.; visualization, P.S.; supervision, P.K.; project administration, P.K.; funding acquisition, P.K. Both authors have read and agreed to the published version of the manuscript.

**Funding:** This work was financed/co-financed by Military University of Technology under research project UGB 22-856.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.
