We propose robust methods for estimating camera egomotion in noisy,
real-world monocular image sequences in the general case of unknown observer
rotation and translation with two views and a small baseline. This is a
difficult problem because of the nonconvex cost function of the perspective
camera motion equation and because of non-Gaussian noise arising from noisy
optical flow estimates and scene non-rigidity. To address this problem, we
introduce the expected residual likelihood method (ERL), which estimates
confidence weights for noisy optical flow data using likelihood distributions
of the residuals of the flow field under a range of counterfactual model
parameters. We show that ERL is effective at identifying outliers and
recovering appropriate confidence weights in many settings. We compare ERL to a
novel formulation of the perspective camera motion equation using a lifted
kernel, a recently proposed optimization framework for joint parameter and
confidence weight estimation with good empirical properties. We incorporate
these strategies into a motion estimation pipeline that avoids falling into
local minima. We find that ERL outperforms the lifted kernel method and
baseline monocular egomotion estimation strategies on the challenging KITTI
dataset, while adding almost no runtime cost over baseline egomotion methods.

使用期望残差似然方法（ERL）对噪声、光流估计和场景非刚性引起的非高斯噪声进行建模，有效地识别异常值并在许多设置中恢复恰当的置信度权重，从而超过基线单目自我运动估计策略。