融合视觉和激光测距的机器人Monte Carlo自定位方法

针对移动机器人采用单类传感器很难成功定位的问题,提出一种室内环境下基于异质传感器信息融合的粒子滤波自定位方法。建立激光测距仪和视觉传感器各自感知模型后,利用融合的感知信息进行粒子集的更新,从而进行自主定位。实验表明,定位过程中激光测距的快速准确更新特性和视觉信息的全局性得到互补,粒子集比使用单类传感器时收敛得更快,提高了移动机器人的自定位精度和速度。

Robot Monte Carlo Self-localization Method Based on Combination of Vision Sensors and Laser Range Finder

With the aim to deal with the localization disadvantage of robot equipped with only single class sensor, a novel mobile robot particle filter self-localization method based on combination of the heterogeneous sensors was proposed. Perception model of LRF (laser range finder sensor) and monocular camera were established, and self-localization was achieved after the particle sets had been updated with fusion perception information. The experimental results showed that characteristics of fast and accurate updates of LRF and global of monocular camera was fully utilized, convergence time of particle sets was reduced by 14.3% than using a single class of sensor, and mobile robot located accuracy was improved by 16.7%.