Abstract

This study proposes an enhanced localization and mapping algorithm for indoor mobile robots based on Ultra-Wideband (UWB) technology. By integrating UWB with odometry and LiDAR sen- sors through an Extended Kalman Filter (EKF), the developed approach addresses the limitations of conventional Simultaneous Localization and Mapping (SLAM) techniques in warehouse envi- ronments. Whereas conventional SLAM methods rely on relative positioning, the proposed algo- rithm leverages UWB-based absolute measurements to reduce uncertainty and drift in repetitive or low-feature workspaces. First, the linear velocity and angular velocity of the differential robot are determined based on forward kinematics and odometry sensor feedback signals. Additionally, the Time Difference of Arrival (TDoA) technique is used to calculate the distance from the robot to the UWB anchors to determine the absolute position of the robot. Then, the Adaptive Monte Carlo Localization (AMCL) method is used to obtain the global positioning of the robot based on LiDAR sensor data combined with the map server. Finally, the EKF filter combines the above informationto accurately determined the pose of the robot. To evaluate the effectiveness of the algorithm, a numerical simulation is performed to compare the odometry sensor-based positioning algorithm and the proposed odometry, UWB, and EKF sensor-based positioning algorithm in three cases, with the number of UWB anchors increasing from 3 to 5 in a Python environment. Furthermore, a visual simulation is developed within the ROS-Gazebo framework using a differential-drive mobile robot equipped with multiple plug-in sensors to evaluate the improvement of the proposed algorithm. Simulation results from both simulation platforms confirm that the fusion of UWB, odometry, and LiDAR data using EKF substantially improves localization precision and system stability