Skip to Content

Sponsors

No results

Keywords

No results

Types

No results

Search Results

Events

No results
Search events using: keywords, sponsors, locations or event type
When / Where
All occurrences of this event have passed.
This listing is displayed for historical purposes.

Presented By: Michigan Robotics

Multi-Modal Geometric Learning for Robot Localization

Robotics PhD Defense, Chien Erh (Cynthia) Lin

point cloud rotated around y-axis point cloud rotated around y-axis
point cloud rotated around y-axis
Chair: Maani Ghaffari

Abstract:
Robot localization uses external sensor data to estimate a robot's position, but movements can alter vision measurements and learned features, impacting robustness. This thesis introduces a new perspective on multi-modal robot localization through the integration of geometric learning, with a primary focus on equivariant networks to address these challenges. First, we explore the application of geometric learning in the loop closure process, enabling the robust identification of previously visited locations despite transformative changes. Next, we propose SE(3)-equivariant transformer designs for learning point cloud correspondences, crucial for point cloud registration tasks. Finally, we integrate geometric learning with visual foundation models. Overall, this dissertation highlights the advantages of incorporating geometric constraints into machine learning models, significantly enhancing both the reliability and performance of robot localization and contributing to critical advancements in robotics.

Zoom link: https://umich.zoom.us/j/91678338359?pwd=cLzyN6l1MU1exRFE25hKzb2uW9EMmW.1
point cloud rotated around y-axis point cloud rotated around y-axis
point cloud rotated around y-axis

Explore Similar Events

  •  Loading Similar Events...

Back to Main Content