We propose a continuous-time spline-based formulation for visual-inertial odometry (VIO). Specifically, we model the poses as a cubic spline, whose temporal derivatives are used to synthesize linear acceleration and angular velocity, which are compared to the measurements from the inertial measurement unit (IMU) for optimal state estimation. The spline boundary conditions create constraints between the camera and the IMU, with which we formulate VIO as a constrained nonlinear optimization problem. Continuous-time pose representation makes it possible to address many VIO challenges, e.g., rolling shutter distortion and sensors that may lack synchronization. We conduct experiments on two publicly available datasets that demonstrate the state-of-the-art accuracy and real-time computational efficiency of our method.

翻译：我们建议为视觉-肾上腺测量(VIO)制定一个基于连续时间的样板配方。 具体地说,我们以立方样样条来模拟外形,其时间衍生物被用于合成线性加速度和角速度,与惯性测量单位(IMU)为最佳状态估测而提供的测量数据相比,这些外线边界条件在摄像头和IMU之间造成了限制,我们与该单位一道将VIO作为受限制的非线性优化问题来制定。 连续时间代表制使得能够应对许多VIO的挑战,例如滚动的透视器扭曲和感应器可能缺乏同步性。 我们对两种公开的数据集进行了实验,这些数据集显示了我们方法的最新准确性和实时计算效率。