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.

RollingShutter Modelling for Direct VisualInertial Odometry We present a direct visualinertial odometry (VIO) method which estimates the motion of the sensor setup and sparse 3D geometry of the environment based on measurements from a rollingshutter camera and an inertial measurement unit (IMU). The visual part of the system performs a photometric bundle adjustment on a sparse set of points. This direct approach does not extract feature points and is able to track not only corners, but any pixels with sufficient gradient magnitude. Neglecting rollingshutter effects in the visual part severely degrades accuracy and robustness of the system. In this paper, we incorporate a rollingshutter model into the photometric bundle adjustment that estimates a set of recent keyframe poses and the inverse depth of a sparse set of points. IMU information is accumulated between several frames using measurement preintegration, and is inserted into the optimization as an additional constraint between selected keyframes. For every keyframe we estimate not only the pose but also velocity and biases to correct the IMU measurements. Unlike systems with globalshutter cameras, we use both IMU measurements and rollingshutter effects of the camera to estimate velocity and biases for every state. Last, we evaluate our system on a new dataset that contains globalshutter and rollingshutter images, IMU data and groundtruth poses for ten different sequences, which we make publicly available. Evaluation shows that the proposed method outperforms a system where rolling shutter is not modelled and achieves similar accuracy to the globalshutter method on globalshutter data.