We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as Lidar, camera, and IMU and achieve robust state estimation in extremely perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-Centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, visual-inertial odometry, and laser-inertial odometry, but is flexible enough to allow for adding other sensor inputs. The visual-inertial odometry and laser-inertial odometry provide the pose prior to constrain the IMU bias and receive the motion prediction from IMU odometry. To ensure high performance in real-time, we also proposed a novel dynamic octree that only consumes 10% of the running time in a static K-D tree. The proposed system was deployed on drones and ground robots, as part of Team Explorer's effort to the DARPA Subterranean Challenge where the team won 1st and 2nd place in the Tunnel and Urban Circuits, respectively.