Grant: Canada Research Chairs

During the past decade, the use of Autonomous Vehicles Systems (AVS) has experienced continued growth across military and governmental agencies, industry, and academia. Among these AVS, Self-Driving Vehicles “SDV” and Unmanned Aerial Vehicles “UAV” have drawn the most comprehensive attention from many users. The trust of AVS hinges on the reliability of the autonomy including the crucial task of Positioning, Location And Navigation (PLAN) of such vehicles. AVS typically rely on satellite-based Global Navigation Satellite Systems (GNSS), such as GPS, as the core positioning technology. However, the performance of GNSS greatly suffers in degraded environments such as signal blockage, multipath and signal jamming and as a result navigation of current AVS relies more heavily on digital maps and remote sensing sensors (e.g. laser detection and ranging (LiDAR), Cameras, RADAR) to perceive the environment. However, these sensors also suffer in degraded environmental conditions such as low-visibility, rain, and fog. A third category are self-contained/environment-independent navigation sensors such as Inertial Navigation Systems (INS) which measure the motion dynamics using accelerometers and gyroscopes. These sensors can provide PLAN everywhere at a very high rate independent of weather conditions or external signals or infrastructure. However, their performance degrades with time in the absence of updates from external sources. As such, current PLAN techniques for AVS all have advantages and drawbacks; there is no single technique that appears to be sufficiently versatile to address the challenges mentioned above. The CRC research program will advance the development of technologies and processing platforms that enable the deployment of novel PLAN systems operating efficiently in all environments and specifically in degraded environments conditions such as GNSS Signal blockage, darkness, absence of visual features/landmarks, smoke, fog and rain. This will be achieved through multi-sensor fusion of all available sensors to provide continuous and robust positioning anywhere and in all environments and machine intelligence algorithms to manage and coordinate the heterogeneous set of sensors/systems into a robust positioning system to meet the demand for safety-critical autonomous navigation. From autonomy perspective, the chair work is expected to have a large impact on the acceptance and trust of AVS as it will increase its reliability.