Indoor drone location found using four simple range sensors
Monte Carlo-Based Indoor UAV Localization in Dynamic Environments Using Only Four Onboard Range Sensors
Robotics
Summary
Finding where a drone is inside a building is hard, especially when things around are moving. The authors created a new method that lets a drone figure out its 2D position using only four simple distance sensors and its direction. Their approach ignores wrong distance readings and adds extra guesses to fix lost location estimates. Tests show the drone can locate itself within a few centimeters even when obstacles move nearby.
What this means in practice
- •For drone developers: Build indoor drones that navigate with minimal sensors while handling moving obstacles reliably.$Commercial implications: Companies can sell compact indoor drones for inspection or delivery using affordable sensor setups enabled by this method.
- •For robotics engineers: Implement robust 2D localization in mobile robots operating in cluttered indoor environments using limited range data.
Authors
Mohammad Reza Ranjbar Divkoti, A. Pedro Aguiar
Abstract
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.