Uploaded April 2026 | Updated September 2026, 2 weeks ago
Title: Reliable and Efficient Visual-Inertial Estimation and Spatial Perception
Speaker: Chuchu Chen (George Washington University)
Date: Friday, April 17, 2026
Abstract: Visual-inertial systems (VINS), which fuse camera and inertial measurements for motion estimation and spatial perception, have become an important capability for robotics and XR. Due to their low cost, compact size, and complementary sensing modalities, they are particularly attractive for drones, mobile robots, and wearable devices operating under tight sensing, energy, and compute constraints. At the same time, achieving reliable real-time performance on such platforms remains challenging. In this talk, I will present recent work on the foundations of visual-inertial estimation, focusing on estimator consistency, decoupled error and state representations, and efficient visual-inertial odometry for low-cost and low-energy platforms. I will also discuss selected results on multi-sensor calibration and how system design choices affect practical performance in resource-constrained deployment. Finally, I will discuss how these estimation principles connect to spatial perception, including plane-aware estimation and recent efforts toward richer 3D scene representations. Together, these directions support accurate, efficient, and deployable perception for robots operating in complex real-world environments.
Bio: Chuchu Chen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at The George Washington University, where she directs the EPIC Lab (Estimation, Perception, and Intelligent Computing). She received her Ph.D. in Mechanical Engineering and her M.S. in Computer and Information Sciences from the University of Delaware in 2025, where she was a member of the Robot Perception and Navigation Group advised by Prof. Guoquan (Paul) Huang. Her research focuses on state estimation and spatial perception for robotics and XR, with an emphasis on reliable, efficient, and deployable systems under real-world sensing and compute constraints. Her honors include the ICRA 2024 Best Paper Award Finalist (Robot Vision), the RSS 2023 Best Student Paper Award Finalist, and the University of Delaware Doctoral Fellowship for Excellence.
This video is closed captioned.
Title: Reliable and Efficient Visual-Inertial Estimation and Spatial Perception
Speaker: Chuchu Chen (George Washington University)
Date: Friday, April 17, 2026
Abstract: Visual-inertial systems (VINS), which fuse camera and inertial measurements for motion estimation and spatial perception, have become an important capability for robotics and XR. Due to their low cost, compact size, and complementary sensing modalities, they are particularly attractive for drones, mobile robots, and wearable devices operating under tight sensing, energy, and compute constraints. At the same time, achieving reliable real-time performance on such platforms remains challenging. In this talk, I will present recent work on the foundations of visual-inertial estimation, focusing on estimator consistency, decoupled error and state representations, and efficient visual-inertial odometry for low-cost and low-energy platforms. I will also discuss selected results on multi-sensor calibration and how system design choices affect practical performance in resource-constrained deployment. Finally, I will discuss how these estimation principles connect to spatial perception, including plane-aware estimation and recent efforts toward richer 3D scene representations. Together, these directions support accurate, efficient, and deployable perception for robots operating in complex real-world environments.
Bio: Chuchu Chen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at The George Washington University, where she directs the EPIC Lab (Estimation, Perception, and Intelligent Computing). She received her Ph.D. in Mechanical Engineering and her M.S. in Computer and Information Sciences from the University of Delaware in 2025, where she was a member of the Robot Perception and Navigation Group advised by Prof. Guoquan (Paul) Huang. Her research focuses on state estimation and spatial perception for robotics and XR, with an emphasis on reliable, efficient, and deployable systems under real-world sensing and compute constraints. Her honors include the ICRA 2024 Best Paper Award Finalist (Robot Vision), the RSS 2023 Best Student Paper Award Finalist, and the University of Delaware Doctoral Fellowship for Excellence.
This video is closed captioned.
![[Audio Descriptions] I Am CSE Overview](https://i.ytimg.com/vi/kz0b8RCsfKw/mqdefault.jpg "[Audio Descriptions] I Am CSE Overview
In this mash-up of footage from our collection of I Am CSE videos, four Allen School student researchers describe their work in artificial intelligence, robotics, accessibility, and more—and explain why the Allen School is a great place to do leading-edge research.
This video is in the process of being closed captioned.
A version of this video without audio descriptions is available here: https://youtu.be/u2C9HMSU6Qo")
