We compute the uncertainty of XIVO, a monocular visual-inertial odometry system based on the Extended Kalman Filter, in the presence of Gaussian noise, drift, and attribution errors in the feature tracks in addition to Gaussian noise and drift in the IMU. Uncertainty is computed using Monte-Carlo simulations of a sufficiently exciting trajectory in the midst of a point cloud that bypass the typical image processing and feature tracking steps. We find that attribution errors have the largest detrimental effect on performance. Even with just small amounts of Gaussian noise and/or drift, however, the probability that XIVO's performance resembles the mean performance when noise and/or drift is artificially high is greater than 1 in 100.

翻译：我们计算了XIVO（一种基于扩展卡尔曼滤波器的单目视觉-惯性里程计系统）在存在高斯噪声、漂移以及特征轨迹归因误差（除IMU的高斯噪声和漂移外）时的不确定性。不确定性通过蒙特卡洛模拟计算，该模拟采用点云中一条充分激励轨迹，绕过了典型的图像处理和特征跟踪步骤。我们发现归因误差对性能的影响最大。然而，即使仅存在少量高斯噪声和/或漂移，XIVO性能接近人为设定高噪声和/或漂移时的平均性能的概率仍超过1/100。