Li Gen, Meng Jie, Xie Yuanlong, Zhang Xiaolong, Huang Yu, Jiang Liquan, Liu Chao
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel). 2019 Jul 29;19(15):3331. doi: 10.3390/s19153331.
In real-world robotic navigation, some ambiguous environments contain symmetrical or featureless areas that may cause the perceptual aliasing of external sensors. As a result of that, the uncorrected localization errors will accumulate during the localization process, which imposes difficulties to locate a robot in such a situation. Using the ambiguity grid map (AGM), we address this problem by proposing a novel probabilistic localization method, referred to as AGM-based adaptive Monte Carlo localization. AGM has the capacity of evaluating the environmental ambiguity with average ambiguity error and estimating the possible localization error at a given pose. Benefiting from the constructed AGM, our localization method is derived from an improved Dynamic Bayes network to reason about the robot's pose as well as the accumulated localization error. Moreover, a portal motion model is presented to achieve more reliable pose prediction without time-consuming implementation, and thus the accumulated localization error can be corrected immediately when the robot moving through an ambiguous area. Simulation and real-world experiments demonstrate that the proposed method improves localization reliability while maintains efficiency in ambiguous environments.
在现实世界的机器人导航中，一些模糊的环境包含对称或无特征区域，这可能会导致外部传感器的感知混叠。因此，未校正的定位误差会在定位过程中累积，这给在这种情况下定位机器人带来了困难。我们使用模糊网格地图（AGM），通过提出一种新颖的概率定位方法来解决这个问题，该方法称为基于AGM的自适应蒙特卡洛定位。AGM能够通过平均模糊误差评估环境模糊性，并估计给定姿态下可能的定位误差。受益于构建的AGM，我们的定位方法源自改进的动态贝叶斯网络，用于推断机器人的姿态以及累积的定位误差。此外，还提出了一种门限运动模型，以实现更可靠的姿态预测，而无需耗时的实现过程，因此当机器人穿过模糊区域时，累积的定位误差可以立即得到校正。仿真和实际实验表明，该方法在模糊环境中提高了定位可靠性，同时保持了效率。
