EDGES: Improving WLAN SLAM with Logic Graph Construction and Mapping

In recent decade, the Received Signal Strength (RSS) based indoor localization has caught significant attention, but it always suffers from the time-consuming and labor intensive fingerprint calibration. At the same time, the Simultaneous Localization and Mapping (SLAM) technique is considered with the low time and laboring cost, whereas the dedicated hardware is often required. To solve these problems, a novel indoor WLAN SLAM approach by using the Edge Detection based Gene Sequencing (EDGES) is proposed. First of all, a batch of RSS sequences is sporadically collected in target area. Second, the spectral clustering is conducted on RSS sequences to construct the cluster graphs, and then the EDGES approach is applied to assemble the cluster graphs into a logic graph. Finally, the mapping from the logic graph into ground-truth graph is established to realize indoor WLAN SLAM. The extensive experimental results prove that the proposed approach can achieve satisfying localization accuracy without site survey of location fingerprinting or motion sensing.