School of Mechatronics, Beijing Institute of Technology, Beijing 100080, China.
Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing 100080, China.
Sensors (Basel). 2018 Oct 22;18(10):3581. doi: 10.3390/s18103581.
In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate.
对于单一场景特征，室内服务机器人的定位时间较长，并且可能会发生定位误差。提出了一种新的基于多传感器融合的混合室内定位系统方法来解决这些问题。定位过程分为两个阶段：粗略定位和精确定位。本研究首次创建了基于可能性的 K 最近邻（KNNBP）算法，根据 Wi-Fi 的接收信号强度指示符（RSSI）确定机器人的粗略位置。然后，采用在自适应蒙特卡罗定位（AMCL）基础上改进的混合粒子滤波定位（HPFL）算法，结合各种信息，包括粗略位置和激光雷达、罗盘、占据栅格图和编码器的信息，实现精确定位。实验表明，定位误差为 0.05m；在有 3000 个粒子的情况下，定位成功率为 96%，全局定位时间为 1.9s。然而，在相同条件下，AMCL 的成功率约为 40%，所需时间约为 25.6s，定位精度相同。这表明混合室内定位系统高效准确。
