This paper presents the design, implementation and evaluation of milliMap, a
single-chip millimetre wave (mmWave) radar based indoor mapping system
targetted towards low-visibility environments to assist in emergency response.
A unique feature of milliMap is that it only leverages a low-cost,
off-the-shelf mmWave radar, but can reconstruct a dense grid map with accuracy
comparable to lidar, as well as providing semantic annotations of objects on
the map. milliMap makes two key technical contributions. First, it autonomously
overcomes the sparsity and multi-path noise of mmWave signals by combining
cross-modal supervision from a co-located lidar during training and the strong
geometric priors of indoor spaces. Second, it takes the spectral response of
mmWave reflections as features to robustly identify different types of objects
e.g. doors, walls etc. Extensive experiments in different indoor environments
show that milliMap can achieve a map reconstruction error less than 0.2m and
classify key semantics with an accuracy around 90%, whilst operating through
dense smoke.

本文提出了一种基于毫米波雷达的单芯片室内地图系统 milliMap，该系统可用于低能见度环境，以协助紧急响应。milliMap 具有低成本，可通过训练来模拟难以观测的地图，且为地图上的对象提供语义注释。

低成本毫米波雷达实现强韧室内映射透视烟雾

See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar

The ultimate goal of this indoor mapping research is to automatically
reconstruct a floorplan simply by walking through a house with a smartphone in
a pocket. This paper tackles this problem by proposing FloorNet, a novel deep
neural architecture. The challenge lies in the processing of RGBD streams
spanning a large 3D space. FloorNet effectively processes the data through
three neural network branches: 1) PointNet with 3D points, exploiting the 3D
information; 2) CNN with a 2D point density image in a top-down view, enhancing
the local spatial reasoning; and 3) CNN with RGB images, utilizing the full
image information. FloorNet exchanges intermediate features across the branches
to exploit the best of all the architectures. We have created a benchmark for
floorplan reconstruction by acquiring RGBD video streams for 155 residential
houses or apartments with Google Tango phones and annotating complete floorplan
information. Our qualitative and quantitative evaluations demonstrate that the
fusion of three branches effectively improves the reconstruction quality. We
hope that the paper together with the benchmark will be an important step
towards solving a challenging vector-graphics reconstruction problem. Code and
data are available at this https URL

本研究旨在通过智能手机，利用深度神经网络技术重建室内地图，提出了线上勘测算法 FloorNet 核心思路，即将 RGBD 流图像数据分别经过三个大的神经网络模块来提取特征并同时考虑 3D 与 2D 空间信息，这种方法能够有效提高室内平面图的重建准确性。

FloorNet: 三维扫描的平面图重建统一框架

FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans

This paper presents a novel algorithm that utilizes a 2D floorplan to align
panorama RGBD scans. While effective panorama RGBD alignment techniques exist,
such a system requires extremely dense RGBD image sampling. Our approach can
significantly reduce the number of necessary scans with the aid of a floorplan
image. We formulate a novel Markov Random Field inference problem as a scan
placement over the floorplan, as opposed to the conventional scan-to-scan
alignment. The technical contributions lie in multi-modal image correspondence
cues (between scans and schematic floorplan) as well as a novel coverage
potential avoiding an inherent stacking bias. The proposed approach has been
evaluated on five challenging large indoor spaces. To the best of our
knowledge, we present the first effective system that utilizes a 2D floorplan
image for building-scale 3D pointcloud alignment. The source code and the data
will be shared with the community to further enhance indoor mapping research.

该文提出了一种新算法，利用 2D 平面图对全景 RGBD 扫描进行校准，该算法可以大幅减少所需扫描数，并在五个大型室内空间上进行了评估。这是第一个利用 2D 平面图进行大型室内三维点云重建的有效系统，提出的算法使用多模态图像对应线索 (在扫描和平面图之间)，并避免了固有的堆叠偏差