Visual SLAM for Navigation & Mapping

Autonomous vehicles (AVs) promise to enhance safety, reduce emissions, and improve transportation system efficiency and reliability. The growing demand for AVs is shaping the future of the automotive industry by transforming the in-vehicle experience and paving the way for large-scale implementation of autonomous driving. AV technology requires onboard intelligence relying on sensors and systems such as global navigation satellite systems (GNSS), including GPS, vehicle motion sensors and remote sensing systems, including cameras, light detection and ranging (LiDAR) and radar. AVs capable of sensing the environment and navigating without human input require robust, high-precision positioning at the decimeter level of accuracy under all operational environments. During this internship, we will devise a vision-based navigation (VBN) module enabling decimeter level of positioning accuracy relying on centralized visual-inertial odometry supported by HD-Maps aiding and a deep Learning-based outlier rejection to mitigate the effect of dynamic objects. This project will advance visual self-localization and mapping for AVs and will contribute to the autonomous systems research at Queen’s. It will also elevate the intern’s expertise in areas of growing demand.

Faculty Supervisor:

Aboelmagd Noureldin

Student:

Partner:

Indian Institute of Technology Roorkee

Discipline:

Computer science

Sector:

Artificial Intelligence; Automotive; Information and Communications Technology

University:

Queen's University

Program: