Invited Session 5
Internet of Things (IoT) Based Indoor/Outdoor Localization and Tracking
Submission code: jvw28
Zhenghua Chen and Chaoyang Jiang
I Aims & Scope:
Localization and tracking can benefit varies applications, such as auto-driving, robotics, navigation services, etc. Generally, it can be divided into two categories, i.e., outdoor and indoor localization.
Many outdoor location-based modern applications such as intelligent transportation systems, robotics or precision agriculture, require reliable, continuous and precise position information for their successful operation. Global Navigation Satellite Systems (GNSS) is the main source for outdoor localization. However, the reliability of the GPS signal is low, especially in downtown aeras, due to the non-line-of-sight and multipath effect. To meet the navigation requirement, it is essential to fuse GPS information with other sensors such as IMU, compass, LiDAR, cameras, etc. To counteract the main vulnerabilities of the localization/navigation system, within the seamless, anywhere, anytime navigation context, collaborative/cooperative positioning/navigation is the cutting-edge research field of interest.
Since GPS signals are blocked by walls, they cannot be used for indoor localization and tracking. Many IoT sensors can be adopted for indoor localization, such as WiFi, Bluetooth, Inertial sensors, Ultrasound, RFID, light, etc. The selection of sensors may relate to the requirements of the application, such as latency, cost, range, accuracy, etc. With different sensor data, model-based and data-driven algorithms can be used for indoor localization. Multiple sensor fusion is also attractive, considering different sensors may have unique properties and limitations.
This invited session intends to prompt emerging techniques on IoT based indoor/outdoor localization and tracking. All submitted papers will be peer-reviewed and selected based on both their quality and relevance.
Potential topics include, but are not limited to:
Localization/navigation for autonomous vehicles
Cooperative localization for connected intelligent vehicles
SLAM for indoor/outdoor localization/navigation
WiFi based localization and tracking
Bluetooth based localization and tracking
Inertial sensor based localization and tracking
Light sensor based localization and tracking
Machine learning based localization and tracking
Deep learning based localization and tracking
Sensor fusion for localization and tracking
Indoor/outdoor seamless localization
Zhenghua Chen, Institute for Infocomm Research (I2R) A*STAR, Singapore
Chaoyang Jiang, School of Mechanical Engineering, Beijing Institute of Technology, China