VisualInertial OnBoard ThrowandGo Initialization for Micro Air Vehicles We propose an approach to the throwandgo (TnG) problem for micro air vehicles (MAVs) using visual and inertial sensors. The key challenge is the fast onboard initialization of the visual odometry (VO) system, which usually requires user input to recover the visual scale. Our approach is based on the identification of the gravity vector from the acceleration data computed with images of the ground during in free fall. This enables scaling of the poses reconstructed with visual information. The proposed framework use inertial data to control the MAV attitude so the ground is visible after the throw. Using image to image homography a metric scale is estimated with which the MAVxe2x80x99s height is propagated. Unlike existing literature, this approach requires no additional sensor nor user input or prethrow assumptions and can recover from any initial attitude. We show results on both simulation and real data.

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.