Navigating the Challenges of Robot Vision in Dynamic Environments

Today we wrap up our coverage of the 2022 CVPR conference joined by Aljosa Osep, a postdoc at the Technical University of Munich & Carnegie Mellon University. In our conversation with Aljosa, we explore his broader research interests in achieving robot vision, and his vision for what it will look like when that goal is achieved. The first paper we dig into is Text2Pos: Text-to-Point-Cloud Cross-Modal Localization, which proposes a cross-modal localization module that learns to align textual descriptions with localization cues in a coarse-to-fine manner. Next up, we explore the paper Forecasting from LiDAR via Future Object Detection, which proposes an end-to-end approach for detection and motion forecasting based on raw sensor measurement as opposed to ground truth tracks. Finally, we discuss Aljosa’s third and final paper Opening up Open-World Tracking, which proposes a new benchmark to analyze existing efforts in multi-object tracking and constructs a baseline for these tasks.

The complete show notes for this episode can be found at twimlai.com/go/581