The IMU/UWB/odometer fusion positioning algorithm based on EKF

Abstract

High-precision indoor positioning is the basis of factory intelligent management. However, the positioning accuracy will decrease because of the complex environment. This study proposes a multi-sensor fusion framework to fuse the data of Ultra Wide Band (UWB), inertial measurement unit (IMU), and odometer. First fuse the data from UWB and IMU by using EKF to obtain attitude, velocity, and position. And then fuse the speed and output with the odometer output using complementary filtering to increase accuracy. At the same time, the algorithm can output higher frequency positioning data. We evaluate the performance of the algorithm on mobile robots. Experimental data is from a 6-axis IMU and 5 UWB radio sensor devices. And the result shows that the position RMSE of our algorithm is 3.29 centimeters and our comprehensive localization algorithm can increase localization accuracy in complex environments compared with only UWB algorithm.

A Novel Loosely Coupling Fusion Approach of Ultra-Wideband and Wheel Odometry for Indoor Localisation

“…However, the experimental environment is in the ideal LOS environment. The paper [28] shows that in the LOS environment, only using UWB can achieve high positioning accuracy with an RMSE equal to 4.66 cm. However, once the UWB has NLOS errors, even in a weak NLOS environment, the overall positioning accuracy of the fused system will be seriously affected, such as in the papers [24,27], where the RMSE exceeds 20 cm.…”

“…The EKF achieves this by linearising the nonlinear system through the Jacobian matrix, subsequently applying the KF. In research conducted by Li et al [28], the EKF was utilised to integrate UWB, IMU, and odometer data. Notably, under conditions where UWB operated strictly in LOS, the combined system's Root Mean Square Error (RMSE) was 3.29 cm.…”

Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment

“…The map and current robot position are displayed on the GUI, and the navigation stack can be fed with KF-based pose information on demand. There are several ROS packages available for collecting and processing IMU sensor data in mobile robots, such as the ROS IMU package, which provides an implementation of an IMU sensor driver as well as a filter for estimating the robot’s orientation using sensor data [ 27 ]. Robot localization, for example, provides an implementation of EKF for fusing data from multiple sensors, including IMU data, to estimate the robot’s position and orientation [ 28 ].…”