Zuo Cili, Xie Demin, Wu Lianghong, Tang Xiaolong, Zhang Hongqiang
School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China.
School of Electrical and Engineering Hunan Industry Polytechnic, Changsha 410208, China.
Sensors (Basel). 2025 Apr 14;25(8):2471. doi: 10.3390/s25082471.
Regarding the issue of high dependency on odometry in the adaptive Monte Carlo localization (AMCL) algorithm, an improved AMCL algorithm based on the normal distributions transform (NDT) and extended Kalman filter (EKF) is proposed. A virtual motion model is introduced into the AMCL framework to enable pose updates even when the robot has not moved. NDT is used for point cloud matching to estimate virtual displacement and calculate virtual control quantities, which are then fed into the motion model to predict and update particle states when the robot has not moved. Additionally, to avoid the negative impacts of encoder errors and wheel slippage on motion state estimation, the EKF algorithm integrates information from the wheel odometer and inertial measurement unit to estimate the robot's displacement, thereby improving localization accuracy and stability. The performance of the proposed algorithm was experimentally validated in both simulated and real environments and compared with other localization algorithms. Experimental results show that the proposed algorithm can effectively improve localization speed during the cold start phase and enhances localization accuracy and stability throughout the localization process. The proposed method is a potential method for improving the performance of mobile robot localization.
针对自适应蒙特卡洛定位（AMCL）算法中对里程计高度依赖的问题，提出了一种基于正态分布变换（NDT）和扩展卡尔曼滤波器（EKF）的改进AMCL算法。在AMCL框架中引入虚拟运动模型，即使机器人未移动也能进行位姿更新。NDT用于点云匹配，以估计虚拟位移并计算虚拟控制量，当机器人未移动时，将这些虚拟控制量输入运动模型以预测和更新粒子状态。此外，为避免编码器误差和车轮打滑对运动状态估计的负面影响，EKF算法整合来自车轮里程计和惯性测量单元的信息来估计机器人的位移，从而提高定位精度和稳定性。通过在模拟环境和真实环境中进行实验验证了所提算法的性能，并与其他定位算法进行了比较。实验结果表明，所提算法能够在冷启动阶段有效提高定位速度，并在整个定位过程中提高定位精度和稳定性。所提方法是一种提高移动机器人定位性能的潜在方法。
