VisualInertial Odometry Tightly Coupled with Wheel Encoder Adopting Robust Initialization and Online Extrinsic Calibration Combining camera, IMU and wheel encoder is a wise choice for car positioning because of the low cost and complementarity of the sensors. We propose a novel extended visualinertial odometry algorithm tightly fusing data from the above three sensors. Firstly we propose an IMUodometer preintegration approach utilizing complete IMU measurements and wheel encoder readings, to make scale estimation more accurate in subsequent 4degrees of freedom (DoF) optimization. Secondly we develop an original initialization module where encoder readings are fully utilized to refine gravity direction and provide an initial value for camera pose in real scale. Thirdly, we design a computationally efficient online extrinsic calibration method by fixing the linearization point for the rotational component of IMUodometer extrinsic parameters, which is deployed depending on the convergence of accelerometer bias. Experimental results prove the robustness of our initialization module and the accuracy of the whole trajectory, as well as the improvement brought about by online extrinsic calibration. Our program can also run on an Nvidia Jetson TX2 module in real time.

Rapid and Robust Monocular VisualInertial Initialization with Gravity Estimation via Vertical Edges Monocular visualinertial tracking without good initialization easily fails due to its nonlinear nature. Rapid and accurate metric initialization is crucial. In this paper, we propose a novel monocular visualinertial initialization method which can initialize the IMU states, camera poses, and scale in a rapid and robust way. To avoid mixing gravity and accelerometer bias, we propose to use the detected vertical edges to estimate a better gravity. This improves the observability to the underlying problem even without sufficient movement, so we can solve all the states crucial for a good initialization. We evaluate our approach on EuRoC dataset and compare with existing stateoftheart methods. The experimental results demonstrate the effectiveness of the proposed method.