Indoor Spatial Query

Indoor spatial query focuses on efficiently and accurately answering questions about the location and relationships between objects within indoor environments, a challenge distinct from outdoor spatial querying. Current research emphasizes developing robust algorithms and models, including Bayesian filtering techniques and graph-based approaches, to handle noisy sensor data (like RFID) and complex indoor layouts, often incorporating visual information from images or point clouds. This field is crucial for improving location-based services, robotics (e.g., search and rescue), and human-computer interaction within buildings, driving advancements in both data representation and query processing techniques.