Student Talks
Supreeth Achar
Carnegie Mellon University

Active Illumination for the Real World

GHC 8102

Abstract: Active illumination systems use a controllable light source and a light sensor to measure properties of a scene. For such a system to work reliably across a wide range of environments it must be able to handle the effects of global light transport, bright ambient light, interference from other active illumination devices, defocus, and [...]

PhD Thesis Defense
Nathan Brooks
Carnegie Mellon University

Situational Awareness and Mixed Initiative Markup for Human-Robot Team Plans

NSH 1305

Abstract: As robots become more reliable and user interfaces (UI) become more powerful, human-robot teams are being applied to more real world problems. Human-robot teams offer redundancy and heterogeneous capabilities desirable in scientific investigation, surveillance, disaster response, and search and rescue operations. Large teams are overwhelming for a human operator, so systems employ high level [...]

PhD Thesis Defense
Abhinav Shrivastava
Carnegie Mellon University

Discovering and Leveraging Visual Structure for Large-scale Recognition

GHC 8102

Abstract: Our visual world is extraordinarily varied and complex, but despite its richness, the space of visual data may not be that astronomically large. We live in a well-structured, predictable world, where cars almost always drive on roads, sky is always above the ground, and so on. As humans, the ability to learn this structure [...]

PhD Thesis Defense
Venkatraman Narayanan
Carnegie Mellon University

Deliberative Perception

Newell-Simon Hall 3305

Abstract: A recurrent and elementary robot perception task is to identify and localize objects of interest in the physical world. In many real-world situations such as in automated warehouses and assembly lines, this task entails localizing specific object instances with known 3D models. Most modern-day methods for the 3D multi-object localization task employ scene-to-model feature [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Compact Generative Models of Point Cloud Data for 3D Perception

Newell-Simon Hall 3305

Abstract: One of the most fundamental tasks for any robotics application is the ability to adequately assimilate and respond to incoming sensor data. In the case of 3D range sensing, modern-day sensors generate massive quantities of point cloud data that strain available computational resources. Dealing with large quantities of unevenly sampled 3D point data is [...]