论文标题
Landmark2VEC：一种无监督的基于神经网络的地标定位方法
Landmark2Vec: An Unsupervised Neural Network-Based Landmark Positioning Method
论文作者
论文摘要
引入了一种基于神经网络的方法，用于从地标进行的测量值进行无监督地标估计。培训网络所需的测量是代理商从地标观察/接收的信号。 The definition of landmarks, agent, and the measurements taken by agent from landmarks is rather broad here: landmarks can be visual objects, e.g., poles along a road, with measurements being the size of landmark in a visual sensor mounted on a vehicle (agent), or they can be radio transmitters, e.g., WiFi access points inside a building, with measurements being the Received Signal Strength (RSS) heard from them by a mobile device carried by a person （代理人）。然后，地图估计的目的是将地标的位置达到规模，旋转和偏移（即地标的拓扑图）。假设有$ l $地标，则测量值将为$ l \ times 1 $ vectors在该区域收集。然后，将对浅网络进行培训，以学习地图，而无需任何基础真相信息。
A Neural Network-based method for unsupervised landmarks map estimation from measurements taken from landmarks is introduced. The measurements needed for training the network are the signals observed/received from landmarks by an agent. The definition of landmarks, agent, and the measurements taken by agent from landmarks is rather broad here: landmarks can be visual objects, e.g., poles along a road, with measurements being the size of landmark in a visual sensor mounted on a vehicle (agent), or they can be radio transmitters, e.g., WiFi access points inside a building, with measurements being the Received Signal Strength (RSS) heard from them by a mobile device carried by a person (agent). The goal of the map estimation is then to find the positions of landmarks up to a scale, rotation, and shift (i.e., the topological map of the landmarks). Assuming that there are $L$ landmarks, the measurements will be $L \times 1$ vectors collected over the area. A shallow network then will be trained to learn the map without any ground truth information.
