Analysis of GNSS-IMU Lidar Integration for Indoor Positioning Using Unscented Kalman Filter - Amelia Nuri Ila, Qarina Putri; Cahyadi, Mokhamad N.; Asfihani, Tahiyatul; Suhandri, Hendy F.; Taufany, Fadlilatul (Civil Engineering Journal, 2025)
Accurate navigation systems are important in various vehicle applications, both indoors and outdoors. Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) are sensors that are often used in vehicle navigation systems. GNSS has the advantage of providing accurate position and speed information, IMU is able to make measurements without being affected by environmental conditions, and LiDAR sensors can model the environment; however, the limited signal on GNSS in indoor environments results in decreased position accuracy. The development of GNSS-IMU integration has been widely carried out, one of which is by adding a LiDAR sensor. In this study, an improvement will be made to the integration algorithm on Vision RTK2, which produces GNSS-IMU coordinate data, and Backpack Lidar, which can display 3D visualization on the traversed path using the Unscented Kalman Filter (UKF) method to improve navigation accuracy, especially in indoor environments. The results of the study showed that the UKF simulation and free outage conditions showed high accuracy with RMSE of 0.00308 m and 0.00175 m for the Easting and Northing positions and MAE of 0.00088 m and 0.00024 m. However, in outage conditions, the RMSE values were 4.0881 m and 8.6317 m, and MAE of 5.9871 m and 7.4182 m. The results of the 3D point cloud of the LiDAR model that had been georeferenced using the UKF fusion results and the KKH calculation results were validated using a rolling meter. Validation of point cloud processing from the 3D LiDAR model using a rolling meter and georeferencing with KKH calculations showed a small RMSE value, which was 0.3420 m, and 0.0354 m for the distance dimension with a rolling meter. 0.6358 m for georeferenced RMSE using UKF fusion data, and 0.0779 for distance dimension using roll meters. The small RMSE results indicate a high level of agreement between point cloud data and measurements using a rolling meter used as reference data. This study shows that the integration of GNSS-IMU sensors with LiDAR using the UKF method can improve the accuracy and reliability of indoor navigation systems.