School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China.
Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore.
Sensors (Basel). 2020 Jul 16;20(14):3948. doi: 10.3390/s20143948.
In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object's radial velocities estimated by the RFID phase. The method also uses a fixed time series as "sliding time window" to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach.
在室内环境中，目标识别和定位对于人机交互至关重要。视觉或激光传感器可以根据目标的外观实现目标的识别和定位，但这些方法计算量较大，并且对存在障碍物的环境不稳健。射频识别 (RFID) 具有唯一的标签 ID 来识别目标，但无法准确定位。因此，在本文中，融合了 RFID 和激光测距仪的数据，以更好地识别和定位室内环境中的多个动态目标。主要方法是使用激光测距仪估计特定环境中物体的径向速度，并将其与 RFID 相位估计的物体径向速度进行匹配。该方法还使用固定的时间序列作为“滑动时间窗口”，在每个窗口中找到每个 RFID 标签的相似度最高的聚类。此外，在粒子滤波器 (PF) 的更新阶段使用皮尔逊相关系数 (PCC) 来估计每个聚类的移动路径，以提高在存在障碍物的复杂环境中的准确性。通过 SCITOS G5 机器人进行了实验验证。结果表明，该方法在存在障碍物的环境中可以实现 90.18%的匹配率和 0.33m 的定位精度。与基于 Bray-Curtis (BC) 相似度匹配的方法以及基于粒子滤波器的方法相比，该方法有效地提高了室内场景中多个目标的匹配率和定位精度。
