基于规划路径约束的机器人定位方法

路径规划是为机器人生成可行驶路径以实现循迹的过程。因此，机器人的位置应该位于或靠近规划的行驶路径。从而，路径规划可为机器人定位产生重要的约束。该文提出一种规划路径约束的位置概率图 (PI-LPM)模型，该模型通过概率来表征机器人在整个地图范围内所处的位置的可能性。其中，模型中概率密度函数是通过核密度估计 (KDE)方法从表征规划路径的所有数据点生成。在所提出的PI-LPM模型基础上，提出一种规划路径约束的机器人定位新算法 (RL-PPC)来提高机器人定位精度。在该方法中，应用粒子滤波算法来融合所提出的PI-LPM模型和已有的传感器定位方法。融合过程中，从PI-LPM模型中计算得到的概率是分配粒子权重的一个重要因素。实验中分别利用仿真数据和真实数据对所提出的模型与算法进行验证。实验结果表明，所提RL-PPC算法可有效融合PI-LPM模型与主流的定位系统(如GPS和LiDAR定位系统)，并显著提高机器人定位的整体性能。

Robot Localization Based on Planned Path Constraints

Path planning is a step to generate a feasible path for a robot to track along. Locations of the robot are supposed to lie on or at least nearby the planned path, which can thus generate important constraints for robot localization. In this paper, a model, called Path-Induced Location Probability Map (PI-LPM), to exploit such constraint on robot localization is proposed. The proposed PI-LPM model is a Probability Density Function (PDF) over the entire map with the probability to describe the likelihood that the robot is located. The PDF is generated from all the points representing the path by applying the Kernel Density Estimation (KDE) method with each point as a sampling point. Based on the PI-LPM model, a Robot Localization from Planned Path Constraints (RL-PPC) method to enhance robot localization is proposed. In this method, particle filter is applied to fuse the develop PI-LPM model and existing localization methods, where the probability from PI-LPM is an important factor to assign weights to the particles. The proposed method is validated with both simulation and real data. In the experiment, the proposed PI-LPM model is integrated into both GPS and LiDAR based localization systems. Experimental results demonstrate that the RL-PPC method can effectively improve the over-all performance of robot localization.