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：我们计算了基于扩展卡尔曼滤波器的单眼视觉惯性测距系统XIVO的不确定性，其中包括高斯噪声、漂移和特征轨迹中的归因误差以及IMU中的高斯噪声和漂移。使用摆脱了典型图像处理和特征跟踪步骤的点云中的足够激动人心的轨迹的蒙特卡罗模拟来计算不确定性。我们发现，归因误差对性能的影响最大。然而，即使只有少量的高斯噪声和/或漂移，在噪声和/或漂移人工过高的情况下，XIVO性能类似于均值性能的概率大于100分之一。