This article presents a robust and fast odometry with multiple modalities. Our framework is based on two subsystems:the GNSS-IMU subsystem estimates coarse states and provides global constraints, the LiDAR-IMU subsystem refines states and constructs the 3-D map. The state estimation is performed by IESKF without interruption even if partial sensor fails. Moreover, a fast and dynamic frame transformation method and distributed backward propagation are designed for linking world-frame states with global measurements and coping with asynchronism data.

In this paper, we present a LiDAR inertial odometry based on an adaptive voxel grid filter to cope with point density variations in indoor and outdoor mixed environments. Benefiting from the adaptive voxel grid filter we proposed, our system can adjust the voxel resolution automatically according to indoors or outdoors, improving the system performance in mixed environments. Moreover, we design a dynamic initialization for the adaptive voxel grid filter. The initial voxel size is set from coarse to fine with computational efficiency as feedback.