多传感器的移动机器人可定位性估计与自定位

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Title:

Self-localization of mobile robot in dynamic environments based onlocalizability estimation with multi-sensor observation

作者:

孙自飞¹, 钱堃^1,2, 马旭东^1,2, 戴先中^1,2

1. 东南大学 自动化学院, 江苏 南京 210096;
2. 复杂工程系统测量与控制教育部重点实验室, 江苏 南京 210096

Author(s):

SUN Zifei¹, QIAN Kun^1,2, MA Xudong^1,2, DAI Xianzhong^1,2

1. School of Automation, Southeast University, Nanjing 210096, China;
2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing 210096, China

关键词:

动态环境; 自主定位; RGB-D传感器; Fisher信息矩阵; 人体检测; 可定位性; 在线估计; 移动机器人

Keywords:

dynamic environment; self-localization; RGB-D sensor; Fisher information matrix; people-detecting; localizability; online estimation; mobile robot

分类号:

TP24

DOI:

10.11992/tis.201607007

摘要:

针对有人干扰的动态室内环境，利用可定位性估计理论提出一种RGB-D传感器辅助激光传感器的移动机器人可靠自定位方法。利用RGB-D传感器信息快速检测人的位置区域，并通过坐标转换计算激光扫描数据中的动态障碍物影响因子，结合离散化Fisher信息矩阵在线估计观测信息的可定位性矩阵；同时通过预测模型协方差矩阵评价里程计信息的可靠性，从而动态补偿观测信息对粒子集的影响。在典型含多人运动的动态室内环境中实验，结果验证了本文方法能够提高机器人自定位的准确性和可靠性。

Abstract:

Based on the localizability estimation theory, in this paper, we propose a new method for the reliable self-localization of mobile robots in a disturbed dynamic indoor environment by the adoption of an RGB-D sensor to assist the laser scanner. People’s location areas are rapidly detected in RGB-D data, which are then transformed to the laser sensor coordinate to compute the influence of the dynamic obstacles on the laser data. In combination with the discrete Fisher information matrix, we estimate the localizability matrix of the observation information online. In addition, we assess the reliability of the information in odometers by the covariance matrix of the prediction model, thereby dynamically compensating for the effect of the observation information on the particle set. We conducted experiments in a dynamic indoor environment and the results confirm the accuracy and reliability of the proposed robot localization method.