Monte Carlo-Based Indoor UAV Localization in Dynamic Environments Using Only Four Onboard Range Sensors

Overview & Synopsis

Indoor UAV localization with minimal onboard sensing remains challenging, particularly in dynamic environments. We propose augmented filtered Monte Carlo localization (AF-MCL) for 2D localization using only four single-point range sensors and measured heading. AF-MCL rejects range measurements inconsistent with the known static map and adaptively injects particles to recover from localization divergence and kidnapping. Beam classification is performed only at the previous position estimate, making its ray-casting cost independent of the particle count. The particle distribution additionally provides an online measure of localization uncertainty. Real-world experiments in a 2.10x1.63 m environment yield mean position errors of 6.4-7.7 cm under static and dynamic conditions. Simulations in 6x6 m and 10x10 m environments yield 4.5-5.2 cm mean errors; under dynamic obstacles, standard MCL reaches 12.3 cm and reliable MCL 7.7 cm. The results show that robust indoor localization under dynamic obstacles is achievable with only four single-point range measurements and heading.