Fingerprint-based localization improves the positioning performance in
challenging, non-line-of-sight (NLoS) dominated indoor environments. However,
fingerprinting models require an expensive life-cycle management including
recording and labeling of radio signals for the initial training and regularly
at environmental changes. Alternatively, channel-charting avoids this labeling
effort as it implicitly associates relative coordinates to the recorded radio
signals. Then, with reference real-world coordinates (positions) we can use
such charts for positioning tasks. However, current channel-charting approaches
lag behind fingerprinting in their positioning accuracy and still require
reference samples for localization, regular data recording and labeling to keep
the models up to date. Hence, we propose a novel framework that does not
require reference positions. We only require information from velocity
information, e.g., from pedestrian dead reckoning or odometry to model the
channel charts, and topological map information, e.g., a building floor plan,
to transform the channel charts into real coordinates. We evaluate our approach
on two different real-world datasets using 5G and distributed
single-input/multiple-output system (SIMO) radio systems. Our experiments show
that even with noisy velocity estimates and coarse map information, we achieve
similar position accuracies

基于指纹的定位方法在复杂的非直视室内环境中提高了定位精度。然而，现有的通道绘图方法在定位准确性方面落后于指纹定位，并且仍然需要参考样本进行定位和定期数据记录和标记以保持模型的最新。因此，我们提出了一个新的框架，不需要参考位置，只需要速度信息和拓扑地图信息来将通道绘图转换为实际坐标。我们在两个不同的真实数据集上对我们的方法进行了评估，使用了 5G 和分布式单输入 / 多输出系统（SIMO）无线电系统。实验证明，即使有噪声的速度估计和粗糙的地图信息，我们也能够达到类似的位置精度。