Appearance Based SLAM (ASLAM) for Indoor/Outdoor Urban Terrain

The objective of this project is the research of multi-sensor Appearance Based SLAM (ASLAM) system for day/night operations in indoor and outdoor environments. These algorithms would perform place recognition based on multiple sensor data (imagery, laser, and radar) gathered from a UGV (Unmanned Ground Vehicle) as it travels through the environment. When the vehicle returns to a previously visited scene, the ASLAM algorithm will recognize the scene, update its internal representation, report this to the UGV. MDA would benefit greatly from this internship as the research of multi-sensor ASLAM techniques by the intern would contribute to this project greatly. Moreover, the involvement of UBC professors Jim Little and David Lowe, who are experts in robotics and computer vision, would be highly valuable to this project too. 

Intern: 
Amir Valizadeh
Faculty Supervisor: 
Dr. Jim Little and Dr. David Lowe
Province: 
British Columbia
Program: