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首页> 外文会议>2013 13th International Conference on Autonomous Robot Systems >Robot#x0040;factory: Localization method based on map-matching and Particle Swarm Optimization
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Robot#x0040;factory: Localization method based on map-matching and Particle Swarm Optimization

机译：Robot @ factory：基于地图匹配和粒子群优化的定位方法

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摘要

This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory which is a new robotic competition presented in Lisbon 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that is well-behaved. The sensor information is continuously updated in time and space through the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, a particle filter based on Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high. Meaning that the map-matching is unreliable and robot is lost. The experiments conducted in this paper prove the ability and accuracy of the presented technique to localize small mobile robots for this competition. Therefore, extensive results show that the proposed method have an interesting localization capability for robots equipped with a limited amount of sensors.
机译：本文提出了一种新型的小型移动机器人定位方法。拟议的技术是专门为Robot @ Factory设计的，这是在2011年里斯本举行的新型机器人竞赛。实时定位技术诉诸于低成本的红外传感器，地图匹配方法和扩展卡尔曼滤波器（EKF） ）创建行为良好的姿势跟踪系统。传感器信息通过机器人的预期运动在时间和空间上不断更新。然后，将该信息合并到地图匹配优化中，以增加每时每刻可用的传感器信息量。另外，当地图匹配误差很高时，基于粒子群优化（PSO）的粒子过滤器会重新定位机器人。这意味着地图匹配不可靠，并且机器人丢失了。本文进行的实验证明了该技术在本次比赛中定位小型移动机器人的能力和准确性。因此，大量结果表明，该方法对配备有限数量传感器的机器人具有有趣的定位能力。

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