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

翻译：指纹定位方法在具有挑战性的、以非视距（NLoS）为主的室内环境中提升了定位性能。然而，指纹识别模型需要昂贵的生命周期管理，包括初始训练时以及环境变化时定期记录和标注无线电信号。另一种方法是信道图构建（channel charting），该方法通过将相对坐标隐式关联到记录的无线电信号，避免了标注工作。随后，结合参考的真实世界坐标（位置），我们可以利用这种图表执行定位任务。然而，当前的信道图构建方法在定位精度上落后于指纹识别，并且仍需参考样本进行定位、定期记录和标注数据以保持模型更新。为此，我们提出了一种无需参考位置的新型框架。我们仅需利用速度信息（例如来自行人航位推算或里程计的数据）来建模信道图，并结合拓扑地图信息（例如建筑平面图）将信道图转换为真实坐标。我们在两种不同的真实世界数据集上（使用5G和分布式单输入/多输出（SIMO）无线电系统）评估了该方法。实验表明，即使存在噪声速度估计和粗略地图信息，我们的方法也能达到相近的定位精度。