An Efficient and Robust Tightly Coupled Framework for Lidar-IMU Localization and Mapping

In this article, we propose a framework for tightly-coupled lidar inertial odometry, which can achieve highly accuracy in real-time ego-motion estimation and map building of the robot. The estimated motion from inertial measurement unit (IMU) preintegration de-skews point clouds. Then, an initialization method based on MAP estimation is used to align the Lidar frame with the world frame and provide reliable initial values for the system. In order to make the scan matching more accurate, we track the state of each frame, and transform the feature points to the reference frame to build a local map for matching with key frames. To ensure high performance in real-time, an efficient sliding window was used to optimize the keyframes we chose. After obtaining the optimized pose of the keyframe, we further optimize the pose of the normal frame by pose constraints. In addition, loop detection is integrated into our algorithm framework to eliminate accumulated errors, making our algorithm to obtain a globally consistent estimate. The experiment results demonstrate that our method can achieve high accuracy in a variety of indoor and outdoor environments even under fast motion conditions or with insufficient features.