Ko Nak Yong, Kuc Tae-Yong
Department of Electronics Engineering, Chosun University, 375 Seosuk-dong Dong-gu, Gwangju 501-759, Korea.
College of Information and Communication Engineering, Sungkyunkwan University, 300 Cheoncheon-dong Jangan-gu Suwon, Gyeonggi-do 440-746, Korea.
Sensors (Basel). 2015 May 11;15(5):11050-75. doi: 10.3390/s150511050.
This paper proposes a method for mobile robot localization in a partially unknown indoor environment. The method fuses two types of range measurements: the range from the robot to the beacons measured by ultrasonic sensors and the range from the robot to the walls surrounding the robot measured by a laser range finder (LRF). For the fusion, the unscented Kalman filter (UKF) is utilized. Because finding the Jacobian matrix is not feasible for range measurement using an LRF, UKF has an advantage in this situation over the extended KF. The locations of the beacons and range data from the beacons are available, whereas the correspondence of the range data to the beacon is not given. Therefore, the proposed method also deals with the problem of data association to determine which beacon corresponds to the given range data. The proposed approach is evaluated using different sets of design parameter values and is compared with the method that uses only an LRF or ultrasonic beacons. Comparative analysis shows that even though ultrasonic beacons are sparsely populated, have a large error and have a slow update rate, they improve the localization performance when fused with the LRF measurement. In addition, proper adjustment of the UKF design parameters is crucial for full utilization of the UKF approach for sensor fusion. This study contributes to the derivation of a UKF-based design methodology to fuse two exteroceptive measurements that are complementary to each other in localization.
本文提出了一种在部分未知室内环境中进行移动机器人定位的方法。该方法融合了两种类型的距离测量：通过超声波传感器测量的机器人到信标的距离，以及通过激光测距仪（LRF）测量的机器人到其周围墙壁的距离。对于融合，采用了无迹卡尔曼滤波器（UKF）。由于使用LRF进行距离测量时求雅可比矩阵不可行，在这种情况下UKF比扩展卡尔曼滤波器具有优势。信标的位置和来自信标的距离数据是已知的，然而距离数据与信标的对应关系并未给出。因此，所提出的方法还处理了数据关联问题，以确定哪个信标对应给定的距离数据。使用不同的设计参数值集对所提出的方法进行了评估，并与仅使用LRF或超声信标的方法进行了比较。对比分析表明，尽管超声信标分布稀疏、误差大且更新速率慢，但与LRF测量融合时可提高定位性能。此外，适当调整UKF设计参数对于充分利用UKF方法进行传感器融合至关重要。本研究有助于推导一种基于UKF的设计方法，以融合在定位中相互补充的两种外部感知测量。
