Improved UKF-SLAM with Lie Group Operation and Robust Feature Tracking for Motion Vehicles
Abstract This paper proposes an improved simultaneous localization and mapping (SLAM) algorithm based on tightly coupled camera images and IMU data, which provides accurate and robust localization for autonomous vehicles and unmanned aerial vehicles (UAV), especially for those in GPS-denied environments. Many research efforts have demonstrated the effectiveness of fusing camera images and inertial data with the Unscented Kalman filter (UKF), but there is still one tricky problem about the non-linearity of the kinematics of rotations. To address this issue, we propose a novel UKF-SLAM approach by rebuilding system and measurement models based on the Lie group and Lie algebra, which obtains state estimates with reasonably high accuracy. Besides, we also offer a new method to handle corner matching outliers, which only causes slightly additional computation costs but eliminates outliers and enhances corner tracking robustness. Results from extensive experimental data have validated the effectiveness of the proposed approach, and this method also achieves comparable precision to the state-of-art.