Event
Indoor UAV Localization Using Four Ranging Sensors
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Mohammad Reza Ranjbar Divkoti and A. Pedro Aguiar propose an augmented filtering Monte Carlo localization method (AF-MCL) that achieves two-dimensional indoor UAV localization using only four single-point ranging sensors and measured heading. The method rejects ranging data inconsistent with a known static map and adaptively injects particles to cope with localization divergence and the kidnapping problem. In a real 2.10×1.63 meter environment, the average position error under static and dynamic conditions was 6.4–7.7 centimeters; in 6×6 meter and 10×10 meter simulated environments, the average error was 4.5–5.2 centimeters. The authors report that under dynamic obstacles, the average error was 12.3 centimeters for standard MCL and 7.7 centimeters for reliable MCL.
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- AF-MCL: Indoor UAV Localization in Dynamic Environments Using Only Four Onboard Range Sensors
The authors propose augmented filtered Monte Carlo localization (AF-MCL), which achieves 2D indoor UAV localization using only four single-point range sensors and measured heading, rejects range measurements inconsistent with a known static map, and adaptively injects particles to handle localization divergence and kidnapping.
arXiv cs.RO ·