We present a novel hybrid (model- and learning-based) architecture for fusing
the most significant features from conventional aerial images and integral
aerial images that result from synthetic aperture sensing for removing
occlusion caused by dense vegetation. It combines the environment's spatial
references with features of unoccluded targets. Our method out-beats the
state-of-the-art, does not require manually tuned parameters, can be extended
to an arbitrary number and combinations of spectral channels, and is
reconfigurable to address different use-cases.

使用混合架构，通过从合成孔径感测得到的整体空中图像和传统空中图像提取最重要的特征，以消除由于茂密植被引起的遮挡。该方法结合了环境的空间参考和无遮挡目标的特征，不需要手动调整参数，可以扩展到任意数量和组合的光谱通道，可根据不同用例进行重新配置，超越了现有技术。

单一与综合多光谱航空影像的融合

Fusion of Single and Integral Multispectral Aerial Images

We present VAPOR, a novel method for autonomous legged robot navigation in
unstructured, densely vegetated outdoor environments using Offline
Reinforcement Learning (RL). Our method trains a novel RL policy from unlabeled
data collected in real outdoor vegetation. This policy uses height and
intensity-based cost maps derived from 3D LiDAR point clouds, a goal cost map,
and processed proprioception data as state inputs, and learns the physical and
geometric properties of the surrounding vegetation such as height, density, and
solidity/stiffness for navigation. Instead of using end-to-end policy actions,
the fully-trained RL policy's Q network is used to evaluate dynamically
feasible robot actions generated from a novel adaptive planner capable of
navigating through dense narrow passages and preventing entrapment in
vegetation such as tall grass and bushes. We demonstrate our method's
capabilities on a legged robot in complex outdoor vegetation. We observe an
improvement in success rates, a decrease in average power consumption, and
decrease in normalized trajectory length compared to both existing end-to-end
offline RL and outdoor navigation methods.

通过离线强化学习，基于高度、强度和密度的 3D LiDAR 点云，目标成本图和处理过的自体感知数据作为状态输入，我们提出了一种用于无结构、密集植被的室外环境中自主四足机器人导航的新方法。通过满足机器人运动特性且避免困在植被中，我们的方法在复杂的室外植被中展示了显著的成功率提升、平均功耗减少和轨迹长度正常化减少。