A Novel Semantic Matching Method for Indoor Trajectory Tracking

Abstract

The rapid development of smartphone sensors has provided rich indoor pedestrian trajectory data for indoor location-based applications. To improve the quality of these collected trajectory data, map matching methods are widely used to correct trajectories. However, these existing matching methods usually cannot achieve satisfactory accuracy and efficiency and have difficulty in exploiting the rich information contained in the obtained trajectory data. In this study, we proposed a novel semantic matching method for indoor pedestrian trajectory tracking. Similar to our previous work, pedestrian dead reckoning (PDR) and human activity recognition (HAR) are used to obtain the raw user trajectory data and the corresponding semantic information involved in the trajectory, respectively. To improve the accuracy and efficiency for user trajectory tracking, a semantic-rich indoor link-node model is then constructed based on the input floor plan, in which navigation-related semantics are extracted and formalized for the following trajectory matching. PDR and HAR are further utilized to segment the trajectory and infer the semantics (e.g., "Turn left", "Turn right", and "Go straight"). Finally, the inferred semantic information is matched with the semantic-rich indoor link-node model to derive the correct user trajectory. To accelerate the matching process, the semantics inferred from the trajectory are also assigned weights according to their relative importance. The experiments confirm that the proposed method achieves accurate trajectory tracking results while guaranteeing a high matching efficiency. In addition, the resulting semantic information has great application potential in further indoor location-based services.

Applying Movement Constraints to BLE RSSI-Based Indoor Positioning for Extracting Valid Semantic Trajectories

“…where each vector is attached with a semantic position s (t) at timestamp t. Thus, we can define T as T =< (t 1 , X (1) , s (1) ), (t 2 , X (2) , s (2) ), ..., (t |T| , X (|T |) , s (|T |) ) >.…”

“…A nonsequential reference set R is the set of RSSI vectors paired with semantic positions without sequential relationship. Thus, R = {(X (1) , s (1) ), (X (2) , s (2) ), ..., (X (|R |) , s (|R |) )}, where |R | denotes the number of collected RSSI vectors. Example 3.…”

“…Similarly, for the second beacon, we get −95 from {(−95, −98, 0}. Then, we get 0.6s, {−93, −95} as the first aggregated observation t 1 ,X (1) . Using the same procedure, we get t 2 ,X (2) = 1s, {−93, −94} as the second aggregated observation.…”

“…Then, we continuously concatenate t t ,ŝ (t) from the beginning until the end of trajectory T as the predicted trajectoryŜT. We denote the inferred semantic trajectory byŜT = < (t 1 ,ŝ (1) ), (t 2 ,ŝ (2) ), ..., (t |ŜT| ,ŝ (|ŜT|) ) >. Note that all of the studied indoor positioning techniques perform in real time.…”

“…Indoor location-based services have given rise to the requirement of establishing various safety measures in IoT (Internet of Things)-enabled smart buildings in recent years. Indoor trajectories [1] support these services for several applications, such as people behavior analytics, movement patterns extraction, next-to-visit recommendations, and hotspot detection. Positioning devices, such as Wi-Fi [2], BLE (Bluetooth Low Energy) [3][4][5][6][7][8], and RFID (radio frequency identification) [9], provide the trajectories using indoor positioning techniques.…”