Tightly Coupled 3D Lidar Inertial Odometry and Mapping
This work addresses the problem of reliable and precise localization for mobile robotics, particularly in degraded environments, representing an incremental improvement in sensor fusion techniques.
The paper tackles ego-motion estimation for mobile robots by introducing a tightly coupled lidar-IMU fusion method that minimizes drift and performs well in challenging conditions, achieving high-precision pose estimation at the IMU update rate even under fast motion or with insufficient features.
Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.