For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

中文翻译：

---

基于卡尔曼滤波和扩展卡尔曼滤波的同时定位与制图

在过去的二十多年中，同时定位和地图绘制（SLAM）问题已引起研究人员的更多关注，并且仍然是机器人技术中的重要话题。当前，已经研究了移动机器人SLAM的各种算法。但是，基于概率的移动机器人SLAM算法通常在未知环境中使用。在本文中，作者提出了两种主要的定位算法。首先是线性卡尔曼滤波器（KF）SLAM，它由五个阶段组成，例如（a）具有绝对测量值的静止机器人，（b）具有绝对测量值的运动车辆，（c）具有相对测量值的静止机器人，（d）移动（e）在没有检测到机器人位置的情况下以相对测量值移动车辆。第二种定位算法是带有扩展卡尔曼滤波器（EKF）的SLAM。最后，通过仿真对提出的SLAM算法进行了测试，以证明其有效性和可行性。仿真结果表明，所提出的SLAM方法可以准确定位地标和移动机器人。