We propose a novel direct sparse visual odometry formulation. It combines a
fully direct probabilistic model (minimizing a photometric error) with
consistent, joint optimization of all model parameters, including geometry --
represented as inverse depth in a reference frame -- and camera motion. This is
achieved in real time by omitting the smoothness prior used in other direct
methods and instead sampling pixels evenly throughout the images. Since our
method does not depend on keypoint detectors or descriptors, it can naturally
sample pixels from across all image regions that have intensity gradient,
including edges or smooth intensity variations on mostly white walls. The
proposed model integrates a full photometric calibration, accounting for
exposure time, lens vignetting, and non-linear response functions. We
thoroughly evaluate our method on three different datasets comprising several
hours of video. The experiments show that the presented approach significantly
outperforms state-of-the-art direct and indirect methods in a variety of
real-world settings, both in terms of tracking accuracy and robustness.

提出了一种新的直接稀疏视觉里程计公式，将全直接概率模型（最小化光度误差）与所有模型参数（包括几何结构 - 在参考帧中表示为反深度 - 和相机运动）的一致联合优化相结合。