论文标题
无线信号强度映射的距离不变稀疏自动编码器
Distance Invariant Sparse Autoencoder for Wireless Signal Strength Mapping
论文作者
论文摘要
基于无线信号强度的定位可以使用廉价的传感器为机器人实现强大的定位。为此，必须为环境中的每个访问点学习一个位置到信号的映射。由于在大多数环境中无线网络的普遍存在，这可能会导致数十或数百个地图。为了降低此问题的维度，我们采用自动编码器，这是一种流行的无监督方法来提取功能提取和数据压缩。特别是，我们建议使用稀疏的自动编码器，这些自动编码器学习潜在空间，以保留输入之间的相对距离。输入和潜在空间之间的距离不变性使我们的系统能够成功地学习紧凑的表示，这些表示允许精确的数据重建，但在使用来自潜在空间而不是输入空间的地图时，对本地化性能的影响也很小。我们通过在室外环境中进行实验来证明我们的方法的可行性。
Wireless signal strength based localization can enable robust localization for robots using inexpensive sensors. For this, a location-to-signal-strength map has to be learned for each access point in the environment. Due to the ubiquity of Wireless networks in most environments, this can result in tens or hundreds of maps. To reduce the dimensionality of this problem, we employ autoencoders, which are a popular unsupervised approach for feature extraction and data compression. In particular, we propose the use of sparse autoencoders that learn latent spaces that preserve the relative distance between inputs. Distance invariance between input and latent spaces allows our system to successfully learn compact representations that allow precise data reconstruction but also have a low impact on localization performance when using maps from the latent space rather than the input space. We demonstrate the feasibility of our approach by performing experiments in outdoor environments.
