We present a direct visual-inertial odometry (VIO) method which estimates the motion of the sensor setup and sparse 3D geometry of the environment based on measurements from a rolling-shutter 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 rolling-shutter effects in the visual part severely degrades accuracy and robustness of the system. In this paper, we incorporate a rolling-shutter 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 global-shutter cameras, we use both IMU measurements and rolling-shutter effects of the camera to estimate velocity and biases for every state. Last, we evaluate our system on a novel dataset that contains global-shutter and rolling-shutter images, IMU data and ground-truth 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 global-shutter method on global-shutter data.

本文提出一种直接的视觉惯性里程计方法（VIO），该方法基于来自快门滚动摄像机和惯性测量单元（IMU）的测量，估计传感器设置的运动和环境的稀疏3D几何图形，并结合光度束调整，估计一组最近的关键帧姿势和稀疏点的反深度，同时利用IMU信息，基于滚动快门效应对速度和偏差进行校准。在公开数据集上进行的实验说明，与不考虑滚动快门的系统相比，所提出的方法表现更好，并在全局快门数据上实现了与全局快门方法类似的准确度。

基于滚动快门的直接视觉惯性里程计建模