BA-LINS: A Frame-to-Frame Bundle Adjustment for LiDAR-Inertial Navigation

Bundle adjustment (BA) has been proven to improve the accuracy of the LiDAR mapping, but has not yet been properly employed in a dead-reckoning navigation system. In this paper, we present a frame-to-frame (F2F) BA for LiDAR-inertial navigation, named BA-LINS. Based on the direct F2F point-cloud association method, the same-plane points are associated among the LiDAR keyframes. Hence, the F2F plane-point BA measurement model can be constructed using the same-plane points. The LiDAR BA and the inertial measurement unit (IMU)-preintegration measurements are tightly coupled under the framework of factor graph optimization. Meanwhile, an effective adaptive covariance estimation algorithm for LiDAR BA measurements is proposed to improve the accuracy further. Exhaustive experiment results on public and private datasets demonstrate that BA-LINS yields superior accuracy to state-of-the-art methods. Compared to the baseline system FF-LINS, the absolute translation accuracy and state-estimation efficiency of BA-LINS are improved by 29.5% and 28.7%, respectively. Besides, the proposed adaptive covariance estimation algorithm exhibits notably improved accuracy and robustness.