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Study on the effects of camera resolution in Visual Odometry
Study on the effects of camera resolution in Visual Odometry
Keywords: Computer Vision, Visual Odometry, SLAM
Visual Odometry (VO) algorithms have gone beyond academic research and are now widely used in the real world. Robotics and AR/VR applications, among many others, rely on VO to estimate the ego motion of the camera. Hardware and software co-design is key to develop accurate and robust algorithms. In this project, we will investigate how design choices at the hardware level affect the VO performance. In particular, we will study how the camera resolution affects the accuracy and robustness of some of the state-of-art VO pipelines.
We believe that the results of this project will help academic research and companies in the hardware and software co-design of VO solutions and expand the use of VO algorithms in commercial products.
Visual Odometry (VO) algorithms have gone beyond academic research and are now widely used in the real world. Robotics and AR/VR applications, among many others, rely on VO to estimate the ego motion of the camera. Hardware and software co-design is key to develop accurate and robust algorithms. In this project, we will investigate how design choices at the hardware level affect the VO performance. In particular, we will study how the camera resolution affects the accuracy and robustness of some of the state-of-art VO pipelines. We believe that the results of this project will help academic research and companies in the hardware and software co-design of VO solutions and expand the use of VO algorithms in commercial products.
Get familiar with VO pipelines and simulation tools. Generate a high-resolution dataset including different camera motions. Benchmark some of the state-of-the-art VO pipelines on this dataset as well as real-world ones. We look for students with strong programming (C++ preferred) and computer vision (ideally have taken Prof. Scaramuzza's class) background.
Get familiar with VO pipelines and simulation tools. Generate a high-resolution dataset including different camera motions. Benchmark some of the state-of-the-art VO pipelines on this dataset as well as real-world ones. We look for students with strong programming (C++ preferred) and computer vision (ideally have taken Prof. Scaramuzza's class) background.
Giovanni Cioffi (cioffi@ifi.uzh.ch), Manasi Muglikar (muglikar@ifi.uzh.ch)
Giovanni Cioffi (cioffi@ifi.uzh.ch), Manasi Muglikar (muglikar@ifi.uzh.ch)