Tightly coupled laser-inertial pose estimation and map building based on B-spline curves

Abstract

Abstract Simultaneous localization and mapping (SLAM) plays a key role in 3D environment modeling and mobile robot environment perception. However, the traditional discrete-time Laser-inertial SLAM methods are not robust due to the imbalanced registration steps between a single LiDAR frame and the global map. This paper proposes a tightly coupled laser-inertial pose estimation and map building method that uses B-spline curves to represent continuous-time trajectory and achieve high robustness of the registration steps. To ensure efficiency, the proposed method separates the SLAM task into an odometer module and a mapping module. The odometer module performs a coarse pose estimation, while the mapping module performs a fine one and builds a global map with 3D LiDAR points. B-spline curves are utilized to integrate both IMU measurement constraints and LiDAR point constraints in the proposed mapping module, which can enhance the association of consecutive LiDAR frames in the optimization step. Besides, the explicit expression of the Jacobi matrix derivation for B-spline-based laser residuals is also introduced to furtherly improve the computation efficiency. Both indoor and outdoor experiments are conducted on a self-collected dataset and a public dataset. Experimental results show that the proposed method can achieve superior performance than the baseline method LIO-mapping.