The intrinsic capability to perceive depth of field and extract salient
information by the Human Vision System (HVS) stimulates a pilot to perform
manual landing over an autoland approach. However, harsh weather creates
visibility hindrances, and a pilot must have a clear view of runway elements
before the minimum decision altitude. To help a pilot in manual landing, a
vision-based system tailored to localize runway elements likewise gets
affected, especially during crosswind due to the projective distortion of
aircraft camera images. To combat this, we propose to integrate a prompt-based
climatic diffusion network with a weather distillation model using a novel
diffusion-distillation loss. Precisely, the diffusion model synthesizes
climatic-conditioned landing images, and the weather distillation model learns
inverse mapping by clearing those visual degradations. Then, to tackle the
crosswind landing scenario, a novel Regularized Spatial Transformer Networks
(RuSTaN) learns to accurately calibrate for projective distortion using
self-supervised learning, which minimizes localization error by the downstream
runway object detector. Finally, we have simulated a clear-day landing scenario
at the busiest airport globally to curate an image-based Aircraft Landing
Dataset (AIRLAD) and experimentally validated our contributions using this
dataset to benchmark the performance.

人眼视觉系统与基于视觉的系统，气象扩散网络与天气提取模型，规范化空间变换网络等方面的研究

带正规化空间变换网络的视觉语言建模，用于飞机的全天候侧风着陆

Vision-Language Modeling with Regularized Spatial Transformer Networks  for All Weather Crosswind Landing of Aircraft

In recent years, the effective and safe collaboration between humans and
machines has gained significant importance, particularly in the Industry 4.0
scenario. A critical prerequisite for realizing this collaborative paradigm is
precisely understanding the robot's 3D pose within its environment. Therefore,
in this paper, we introduce a novel vision-based system leveraging depth data
to accurately establish the 3D locations of robotic joints. Specifically, we
prove the ability of the proposed system to enhance its current pose estimation
accuracy by jointly learning to forecast future poses. Indeed, we introduce the
concept of Pose Nowcasting, denoting the capability of a system to exploit the
learned knowledge of the future to improve the estimation of the present. The
experimental evaluation is conducted on two different datasets, providing
state-of-the-art and real-time performance and confirming the validity of the
proposed method on both the robotic and human scenarios.

近年来，人与机器之间的有效和安全协作在第四工业革命场景中变得极为重要。而实现这种协作范式的一个关键前提就是准确理解机器人在其环境中的三维姿态。因此，在本文中，我们介绍了一种利用深度数据来准确确定机器人关节的三维位置的新型基于视觉的系统。具体而言，我们证明了所提出的系统通过共同学习预测未来姿态的能力，能够提高当前姿态估计的准确性。事实上，我们引入了姿态预测的概念，即系统利用对未来的学习知识来改善对当前姿态的估计能力。我们在两个不同的数据集上进行了实验评估，展示出最先进且实时的性能，并验证了所提出方法在机器人和人类场景下的有效性。

机器人姿态预报：预测未来改善现在

Robot Pose Nowcasting: Forecast the Future to Improve the Present

We explore a novel method to perceive and manipulate 3D articulated objects
that generalizes to enable a robot to articulate unseen classes of objects. We
propose a vision-based system that learns to predict the potential motions of
the parts of a variety of articulated objects to guide downstream motion
planning of the system to articulate the objects. To predict the object
motions, we train a neural network to output a dense vector field representing
the point-wise motion direction of the points in the point cloud under
articulation. We then deploy an analytical motion planner based on this vector
field to achieve a policy that yields maximum articulation. We train the vision
system entirely in simulation, and we demonstrate the capability of our system
to generalize to unseen object instances and novel categories in both
simulation and the real world, deploying our policy on a Sawyer robot with no
finetuning. Results show that our system achieves state-of-the-art performance
in both simulated and real-world experiments.

采用基于视觉的系统和神经网络，我们提出了一种使用密集的向量场来预测各种关节物体的潜在运动的方法，并在此基础上展开手部的运动规划用于操纵各种对象，实验结果表明该系统在模拟和现实世界中均取得了最优良好的表现。