基本信息
浏览量:176
职业迁徙
个人简介
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.
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.
研究兴趣
论文共 189 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
CoRR (2024)
引用0浏览0EI引用
0
0
CoRR (2024)
引用0浏览0EI引用
0
0
CoRR (2023): 304-311
arXiv (Cornell University) (2023): 3086-3095
CVPR 2024 (2023)
引用0浏览0EI引用
0
0
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn