PhD Thesis Proposal
Carnegie Mellon University
Depth Imaging for Navigation in Challenging Environments
Abstract: Depth sensors for robust navigation must measure scenes in darkness, bright light, and in scattering media. Scanning LIDAR devices are the most robust to these conditions, but capture sparse measurements, are slow, and expensive. Consumer depth cameras, on the other hand, are inexpensive and produce dense, high rate depth measurements, but fail in bright [...]
Carnegie Mellon University
Observing Humans In Their Natural Habitat: Data, Algorithms, and Analysis
Abstract: Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. First, we need to give computers insight [...]
Carnegie Mellon University
Analysis of Spatio-Temporally Varying Features in Optical Coherence Tomographic (OCT) and Ultrasound (US) Image Sequences
Abstract: Optical Coherence Tomography (OCT) and Ultrasound (US) are non-ionizing and non-invasive imaging modalities that are clinically used to visualize anatomical structures in the body. OCT has been widely adopted in clinical practice due to its micron-scale resolution to visualize in-vivo structures of the eye. Ultra-High Frequency Ultrasound (UHFUS) can capture images at a depth [...]
Carnegie Mellon University
Planning for Energy-Efficient Coverage and Exploratory Deviation by Robots in Rivers
Abstract: Manual collection of environmental data over a large area can be a time-consuming, costly, and even dangerous process, making it a perfect candidate for automation with mobile robots. Despite this clear suitability and numerous advances in robotics resulting in decreased costs, improved reliability, and increased ease of use, the problem of powering autonomous robots [...]
Carnegie Mellon University
Learning to Forecast Egocentric and Allocentric Behavior in Diverse Domains
Abstract: Reasoning about the future is fundamental to intelligence. In this work, I consider the problem of reasoning about the future actions of an intelligent agent. This poses two key questions. How can we build learning-based systems to forecast the behavior of observed agents (third-person, "allocentric forecasting")? More challenging is the question: how should we [...]
Carnegie Mellon University
Search-based Robust Motion Planning under Uncertainty Guided by Multiple Heuristics
Abstract: Motion planning has achieved a great success in many robotic applications but still suffers in the real world under ample uncertainty. For example, manipulation involves interaction with unstructured and stochastic environments, which results in motion uncertainty. Perception that provides understanding of the environment is also not perfect, which in turn leads to sensing uncertainty. [...]
Learning Multi-Modal Navigation for Unmanned Ground Vehicles
Abstract: A robot that operates efficiently in a team with a human in an unstructured outdoor environment must be able to translate commands from a modality that is intuitive to its operator into actions. This capability is especially important as robots become ubiquitous and interact with untrained users. For this to happen, the robot must [...]
Carnegie Mellon University
Scaling up Self-Supervision for Robot Learning
Abstract: A general purpose robot will need to interact with objects in cluttered environments with minimal supervision. Machine learning provides methods that can deal with these complex tasks without explicitly modelling the environment. More recently, deep learning techniques combined with large scale data has revolutionized the fields of computer vision, language processing and reinforcement learning. [...]
Carnegie Mellon University
MRFMaps: A Representation for Multi-Hypothesis Dense Volumetric SLAM
Abstract: Robust robotic flight requires tightly coupled perception and control. Conventional approaches employ a SLAM algorithm to infer the most likely trajectory and then generate an occupancy grid map using dense sensor data for planning purposes. In such approaches all the robustness and accuracy costs are offset to the SLAM algorithm; if there are any [...]
Carnegie Mellon University
Sparse and Dense Methods for Underwater Localization and Mapping with Imaging Sonar
Abstract: Imaging sonars have been used for a variety of tasks geared towards increasing autonomy of underwater vehicles: image registration and mosaicing, vehicle localization, object recognition, mapping, and path planning, to name a few. However, the complexity of the image formation has led many algorithms to make the restrictive assumption that the scene geometry is [...]