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。