cs.RO · 2610.39708 Copy arXiv ID · Oct 29, 2026 Save AFast Robot Localization Method Based on Monte Carlo Sampling Authors: He Zhaoqing , Hang Li , Xian Pu , Li Wang
Organizations: China Academy of Information and Communications Technology, Beijing, 100191, China
In Abstract Localization is an essential capability of intelligent robots. However, in real-world constrained environments, robots often cannot rely on external localization systems. Therefore, this paper proposes an online particle-filter localization method based on Monte Carlo sampling. The method is applied to the localization of a mobile robot in a maze environment and requires only four range sensors mounted on the front, rear, left, and right sides of the vehicle, without the need for any heading information. Simulation experiments demonstrate that even in a maze where local regions are highly similar, the vehicle can still achieve relatively accurate localization, thereby providing a fast and effective approach for robot localization in complex environments.
More in this topic Robot localization estimates a robot’s position and orientation from onboard sensors, maps, and observations of its surroundings.
See all 179 papers → Oct 23, 2026 · Mohammad Reza Ranjbar Divkoti, A. Pedro Aguiar Robot Localization Particle Filtering
SYSTEC-ARISE Research Center for Systems and Technologies, Faculty of Engineering, University of Porto 4200-465 Porto, Portugal.
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Jul 20, 2026 · Michael M. Bilevich, Tomer Buber, Dan Halperin Mobile Robotics Robot Localization
Blavatnik School of Computer Science and Artificial Intelligence, Tel-Aviv University, Israel.
Oct 23, 2026 J/K move · Enter open · S save
Mohammad Reza Ranjbar Divkoti, A. Pedro Aguiar
SYSTEC-ARISE Research Center for Systems and Technologies, Faculty of Engineering, University of Porto 4200-465 Porto, Portugal.
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.