The ability to estimate rich geometry and camera motion from monocular
imagery is fundamental to future interactive robotics and augmented reality
applications. Different approaches have been proposed that vary in scene
geometry representation (sparse landmarks, dense maps), the consistency metric
used for optimising the multi-view problem, and the use of learned priors. We
present a SLAM system that unifies these methods in a probabilistic framework
while still maintaining real-time performance. This is achieved through the use
of a learned compact depth map representation and reformulating three different
types of errors: photometric, reprojection and geometric, which we make use of
within standard factor graph software. We evaluate our system on trajectory
estimation and depth reconstruction on real-world sequences and present various
examples of estimated dense geometry.

该研究论文利用学习到的紧凑深度图表示和重构三种不同类型的误差，将光度误差，重投影误差和几何误差应用于标准因子图软件中，将不同的方法统一到概率框架中，实现实时性能的同时对现实世界序列的轨迹估计和深度重建进行评估，并呈现了估计的丰富几何示例。

DeepFactors：实时概率稠密单目 SLAM

DeepFactors: Real-Time Probabilistic Dense Monocular SLAM

Estimating the metric height of a person from monocular imagery without
additional assumptions is ill-posed. Existing solutions either require manual
calibration of ground plane and camera geometry, special cameras, or reference
objects of known size. We focus on motion cues and exploit gravity on earth as
an omnipresent reference 'object' to translate acceleration, and subsequently
height, measured in image-pixels to values in meters. We require videos of
motion as input, where gravity is the only external force. This limitation is
different to those of existing solutions that recover a person's height and,
therefore, our method opens up new application fields. We show theoretically
and empirically that a simple motion trajectory analysis suffices to translate
from pixel measurements to the person's metric height, reaching a MAE of up to
3.9 cm on jumping motions, and that this works without camera and ground plane
calibration.

本文提出了一种通过分析运动轨迹和重力引力，从单目图像中测量一个人高度的方法，可以避免额外的假设和数据采集，实验表明，该方法的平均绝对误差可以达到 3.9 厘米。