We present a robot navigation system that uses an imitation learning
framework to successfully navigate in complex environments. Our framework takes
a pre-built 3D scan of a real environment and trains an agent from
pre-generated expert trajectories to navigate to any position given a panoramic
view of the goal and the current visual input without relying on map, compass,
odometry, or relative position of the target at runtime. Our end-to-end trained
agent uses RGB and depth (RGBD) information and can handle large environments
(up to $1031m^2$) across multiple rooms (up to $40$) and generalizes to unseen
targets. We show that when compared to several baselines our method (1)
requires fewer training examples and less training time, (2) reaches the goal
location with higher accuracy, and (3) produces better solutions with shorter
paths for long-range navigation tasks.

本文提出了一种机器人导航系统，采用模仿学习框架在复杂环境下成功导航，使用 RGB 和深度 (RGBD) 信息，可以应对大型环境和多个房间及未知目标的导航，相较于多个基准测试具有更好的性能。

无需地图或指南针的学习：全景目标驱动视觉导航

Learning Your Way Without Map or Compass: Panoramic Target Driven Visual  Navigation

We present a novel, end-to-end approach to align CAD models to an 3D scan of
a scene, enabling transformation of a noisy, incomplete 3D scan to a compact,
CAD reconstruction with clean, complete object geometry. Our main contribution
lies in formulating a differentiable Procrustes alignment that is paired with a
symmetry-aware dense object correspondence prediction. To simultaneously align
CAD models to all the objects of a scanned scene, our approach detects object
locations, then predicts symmetry-aware dense object correspondences between
scan and CAD geometry in a unified object space, as well as a nearest neighbor
CAD model, both of which are then used to inform a differentiable Procrustes
alignment. Our approach operates in a fully-convolutional fashion, enabling
alignment of CAD models to the objects of a scan in a single forward pass. This
enables our method to outperform state-of-the-art approaches by $19.04\%$ for
CAD model alignment to scans, with $\approx 250\times$ faster runtime than
previous data-driven approaches.

一个新的端到端方法可以将嘈杂，不完整的三维扫描转换为干净，完整的物体几何，通过对齐 CAD 模型并在对称感知稠密对象对应预测配对的 Procrustes 坐标系改进，该方法可以单次前向传递地将 CAD 模型与扫描的对象对齐，比以前的数据驱动方法快约 250 倍，并且性能超过现有技术。