EKF Based Localization: Integrating IMU and LiDAR Data in the Hilti SLAM Challenge

With the development of robotics and artificial intelligence technology, autonomous driving vehicles have become an attractive topic in industry and academia. In order to navigate safely, it is necessary for the surrounding environment to form an accurate representation and for the robot to estimate accurately for positioning. Simultaneous Localization and Mapping (SLAM) method is considered as a good solution for localization and navigation of autonomous driving vehicles. One of the most important factors for SLAM is the ability of the robot to make optimal position estimates. In this study, a multi-sensor Hilti SLAM Challenge dataset, which was created in different and challenging scenarios for mobile robot position estimation, was studied on the data taken from a building under construction. Sensor fusion was performed to use IMU and LIDAR data together. Extended Kalman Filter (EKF), a common estimation algorithm, was used to estimate the position of the building. Location estimation results were compared with ground truth values and the method was improved according to the comparison results. A Mean Absolute Error (MAE) value of approximately 2.4 was obtained. It was noted that the position estimates were improved with sensor fusion, the developed algorithm and the simulations performed on the data set containing various difficulties.