Research Interests
Prof. Waslander's current research focuses on two main areas: simultaneous localization and mapping with dynamic camera clusters, and perception for autonomous driving. Dynamic camera clusters are groups of cameras attached to robotic systems in which at least one of the cameras can move relative to the others, such as a gimballed camera common on multirotor drones, or an actuated camera on a mobile manipulator arm. These systems require dynamic calibration to identify an accurate transformation from each camera frame to a base vehicle frame, and so this work has led to minimal parameterizations that provide such transformations based on joint angles for the actuated mechanism. We are developing active vision techniques for both calibration of the dynamic camera cluster and for localization and mapping during operation. This work will enable robotic platforms to exploit their best sensors and reduce the overall sensor requirements by identifying regions of the environment that are most helpful to a given task and focusing sensor attention in those directions.

Perception for autonomous driving involves numerous challenging tasks, such as the identification, localization, tracking and prediction of static and dynamic objects in the environment, the construction of multi-faceted maps for route planning, local path planning and obstacle avoidance, and the localization and state estimation of ego motion. Our research in this area involves both classical and deep learning approaches, and is seeking new ways of extracting estimate uncertainty from deep networks to improve sensor fusion and provide a holistic perceptual representation in real-time on in-vehicle hardware. These efforts are aided by data collection and public road driving evaluations on the Autonomoose testbed, a fully capable autonomous vehicle developed at the University of Waterloo. The team’s emphasis is on robust methods that operate in all weather and lighting conditions and use multiple sources of information to improve both performance and fault tolerance.