We present VI-DSO, a novel approach for visual-inertial odometry, which
jointly estimates camera poses and sparse scene geometry by minimizing
photometric and IMU measurement errors in a combined energy functional. The
visual part of the system performs a bundle-adjustment like optimization on a
sparse set of points, but unlike key-point based systems it directly minimizes
a photometric error. This makes it possible for the system to track not only
corners, but any pixels with large enough intensity gradients. IMU information
is accumulated between several frames using measurement preintegration, and is
inserted into the optimization as an additional constraint between keyframes.
We explicitly include scale and gravity direction into our model and jointly
optimize them together with other variables such as poses. As the scale is
often not immediately observable using IMU data this allows us to initialize
our visual-inertial system with an arbitrary scale instead of having to delay
the initialization until everything is observable. We perform partial
marginalization of old variables so that updates can be computed in a
reasonable time. In order to keep the system consistent we propose a novel
strategy which we call "dynamic marginalization". This technique allows us to
use partial marginalization even in cases where the initial scale estimate is
far from the optimum. We evaluate our method on the challenging EuRoC dataset,
showing that VI-DSO outperforms the state of the art.

本研究提出 VI-DSO，一种同时估计相机姿态和稀疏场景几何的视觉惯性测量新方法，通过最小化组合能量函数中的光度和 IMU 测量误差来进行优化，在 EuRoC 数据集上得到了比现有技术更好的效果。

使用动态边缘化的直接稀疏视觉惯性测程

Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization

Despite learning based methods showing promising results in single view depth
estimation and visual odometry, most existing approaches treat the tasks in a
supervised manner. Recent approaches to single view depth estimation explore
the possibility of learning without full supervision via minimizing photometric
error. In this paper, we explore the use of stereo sequences for learning depth
and visual odometry. The use of stereo sequences enables the use of both
spatial (between left-right pairs) and temporal (forward backward) photometric
warp error, and constrains the scene depth and camera motion to be in a common,
real-world scale. At test time our framework is able to estimate single view
depth and two-view odometry from a monocular sequence. We also show how we can
improve on a standard photometric warp loss by considering a warp of deep
features. We show through extensive experiments that: (i) jointly training for
single view depth and visual odometry improves depth prediction because of the
additional constraint imposed on depths and achieves competitive results for
visual odometry; (ii) deep feature-based warping loss improves upon simple
photometric warp loss for both single view depth estimation and visual
odometry. Our method outperforms existing learning based methods on the KITTI
driving dataset in both tasks. The source code is available at
this https URL

本文探讨了利用立体序列进行深度和视觉里程计学习的方法，并在 KITTI 行驶数据集上的实验表明，我们的方法优于现有的基于学习的方法。