It holds great implications for practical applications to enable
centimeter-accuracy positioning for mobile and wearable sensor systems. In this
paper, we propose a novel, high-precision, efficient visual-inertial (VI)-SLAM
algorithm, termed Schmidt-EKF VI-SLAM (SEVIS), which optimally fuses IMU
measurements and monocular images in a tightly-coupled manner to provide 3D
motion tracking with bounded error. In particular, we adapt the Schmidt Kalman
filter formulation to selectively include informative features in the state
vector while treating them as nuisance parameters (or Schmidt states) once they
become matured. This change in modeling allows for significant computational
savings by no longer needing to constantly update the Schmidt states (or their
covariance), while still allowing the EKF to correctly account for their
cross-correlations with the active states. As a result, we achieve linear
computational complexity in terms of map size, instead of quadratic as in the
standard SLAM systems. In order to fully exploit the map information to bound
navigation drifts, we advocate efficient keyframe-aided 2D-to-2D feature
matching to find reliable correspondences between current 2D visual
measurements and 3D map features. The proposed SEVIS is extensively validated
in both simulations and experiments.

本研究提出了一种基于 Schmidt 卡尔曼滤波的高精度的 VI-SLAM 算法，将 IMU 测量和单目图像紧密耦合，以提供带有有界误差的三维运动跟踪，并通过针对性地选择信息特征来减少计算量，同时仍然允许 EKF 正确地考虑其与活动状态之间的交叉关系，从而实现了线性计算复杂度以适应移动和可穿戴传感器系统。

一种高效的基于 Schmidt-EKF 的三维视觉惯性 SLAM 算法

An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM

We propose a method for generating video-realistic animations of real humans
under user control. In contrast to conventional human character rendering, we
do not require the availability of a production-quality photo-realistic 3D
model of the human, but instead rely on a video sequence in conjunction with a
(medium-quality) controllable 3D template model of the person. With that, our
approach significantly reduces production cost compared to conventional
rendering approaches based on production-quality 3D models, and can also be
used to realistically edit existing videos. Technically, this is achieved by
training a neural network that translates simple synthetic images of a human
character into realistic imagery. For training our networks, we first track the
3D motion of the person in the video using the template model, and subsequently
generate a synthetically rendered version of the video. These images are then
used to train a conditional generative adversarial network that translates
synthetic images of the 3D model into realistic imagery of the human. We
evaluate our method for the reenactment of another person that is tracked in
order to obtain the motion data, and show video results generated from
artist-designed skeleton motion. Our results outperform the state-of-the-art in
learning-based human image synthesis. Project page:
this http URL

该研究提出了一种基于视频的真实人物动画生成方法，利用中等质量的可控 3D 模板模型和神经网络技术，成功实现了人物形象的逼真渲染和编辑。