CollageMap: Tailoring Generative Fingerprint Map via Obstacle-Aware Adaptation for Site-Survey-Free Indoor Localization

As wireless-equipped devices are widely deployed, fingerprint-based indoor localization becomes popular due to its simple yet precise feature. A key challenge is constructing an accurate map of signals with their corresponding coordinates. However, because the structural layout of each location uniquely affects signal propagation from distinct access points (APs), fingerprint maps cannot be transferred to other locations. This leads to localization failure in unexplored areas. In this paper, we propose CollageMap, an obstacle-aware fingerprint map constructor embracing generic signal features and AP-oriented unique features. We tackle the problem of fingerprint construction as a compound of two complementary maps: 1) obstacle-independent universal map reflecting intrinsic propagation patterns; and 2) obstacle-dependent adaptation map representing the extrinsic effect of obstacles. We construct a universal model that learns existing fingerprints in various training locations so that it can be generally used at any other place. On top of the universal map, another deep neural network (DNN) learns the real signal deviations between the universal map and the ground-truth map and generates the compensation as the adaptation map for obstructed environments. Using real-world received signal strength indicator (RSSI) testbeds across various wireless radios, we have validated CollageMap provides outstanding signal pattern estimation even in the presence of obstacles, achieving improvements in localization accuracy of up to 30.36%, 17.95%, and 16.97% using Wi-Fi, ZigBee, and BLE, respectively, via adaptation. CollageMap effectively keeps the performance gap of only 0.42%, 17.43 and 7.10% on average, compared to the ground-truth map obtained from the site survey.