VIW-Fusion: Extrinsic Calibration and Pose Estimation for Visual-IMU-Wheel Encoder System

The data fusion of camera, IMU, and wheel encoder measurements has proved its effectiveness in localizing ground robots, and obtaining accurate sensor extrinsic parameters is its premise. We propose an extrinsic parameter calibration algorithm and a multi-sensor-based pose estimation algorithm for the camera-IMU-wheel encoder system. First, we propose a joint calibration algorithm for the extrinsic parameters of the camera-IMU-wheel encoder system, which improves the accuracy and robustness of the camera-wheel encoder calibration. We then extend the visual-inertial odometry (VIO) to incorporate the measurements from the wheel encoder and weight the wheel encoder measurements according to angular velocity in global optimization to improve the performance. We further propose a novel method for VIO initialization by integrating wheel encoder information, which significantly reduces the scale error in initialization. We conduct extrinsic parameter calibration experiments on a real self-driving car and validate the performance of our multi-sensor-based localization system on the KAIST dataset and a dataset collected by our self-driving vehicles by performing an exhaust comparison with the state-of-the-art algorithms. Our implementations are open source1.