A Fast Robot Localization Method Using Monte Carlo Sampling
AFast Robot Localization Method Based on Monte Carlo Sampling
This paper introduces an online particle-filter localization method for robots using Monte Carlo sampling. It enables accurate localization in maze-like environments with minimal sensors, without heading information.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
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range sensors
4
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s 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.
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s 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.
Open source S3
arXiv:2610.39708v1 Announce Type: new 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 require
Open source S2
Source：arXiv Robotics — research abstracts · arxiv.org