INTERSECT - Multi-Source Sensor Fusion for Object Recognition
INTERSECT (Inter-vehicle sensor fusion for enhanced object classification and situational awareness in cooperative mapping of an a priori unknown environment) is a project that extends the concept of sensor fusion for object recognition and classification to multiple mobile sources with potentially heterogeneous computing capabilities. This will allow a more robust situational awareness in situations where different autonomous vehicles are operating in the same areas.
The core idea of this project is to develop new methods and algorithms that provide a more robust situational awareness of an autonomous agent's environment by means of efficient and effective object detection, classification and movement prediction through the collaboration with other agents operating in the same environment. Multi-source and multi-temporal data fusion are topics that only recently have started to develop. INTERSECT targets to leverage the three-dimensional information available from different points of view, which are given by spatially separated autonomous agents. In particular, research will be directed towards object recognition and classification based on object three-dimensional models that include parameters such as spatial shape, surface texture or temperature. 3D Convolutional Neural Networks are the key for advanced classification of objects based on 3D models and attributes such as colour, texture or temperature.
 Jorge Peña Queralta, Tuan Nguyen Gia, Hannu Tenhunen and Tomi Westerlund, "Collaborative Mapping with IoE-based Heterogeneous Vehicles for Enhanced Situational Awareness", IEEE Sensors Applications Symposium 2019 , IEEE (2019) . (View) (Download) (Researchgate)
 Cassandra McCord, Jorge Peña Queralta, Tuan Nguyen Gia and Tomi Westerlund, "Distributed Progressive Formation Control for Multi-Agent Systems: 2D and 3D deployment of UAVs in ROS/Gazebo with RotorS", European Conference on Mobile Robots (ECMR) , IEEE (2019) . (View) (Download)
 Li Qingqing, Jorge Peña Queralta, Tuan Nguyen Gia, Zhuo Zou, Hannu Tenhunen and Tomi Westerlund, "Detecting Water Reflection Symmetries in Point Clouds for Camera Position Calibration in Unmanned Surface Vehicles", The 19th International Symposium on Communications and Information Technologies (ISCIT) , IEEE (2019) . (View) (Download) (Researchgate)
 L. Qingqing, J. Peña Queralta, T. N. Gia, Z. Zou, T. Westerlund, "Multi Sensor Fusion for Navigation and Mapping in Autonomous Vehicles: Accurate Localization in Urban Environments", Unmanned Systems (2020) . The 9th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and the 9th IEEE International Conference on Robotics, Automation and Mechatronics (RAM). (View) (Download) (Researchgate)
 Li Qingqing, Fu Yuhong, Jorge Pena Queralta, Tuan Nguyen Gia, Hannu Tenhunen, Zhou Zou and Tomi Westerlund, "Edge Computing for Mobile Robots:Multi-Robot Feature-Based Lidar Odometry with FPGAs", The 12th International Conference on Mobile Computing and Ubiquitous Networking (2019) . (View) (Download) (Researchgate)
 Li Qingqing, Jorge Pena Queralta, Tuan Nguyen Gia, Hannu Tenhunen, Zhou Zou and Tomi Westerlund, "Visual Odometry Offloading in Internet of Vehicles with Compression at the Edge of the Network", The 12th International Conference on Mobile Computing and Ubiquitous Networking (2019) . (View) (Download) (Researchgate)