Researcher. Engineer. Problem Solver.
Nathaniel Hanson
PhD Candidate
Northeastern University, Institute for Experiential Robotics
RIVeR Lab
PARSES Lab
Biography
Hello! I am a PhD Candidate in the Computer Engineering program at Northeastern University under the supervision of Kristen Dorsey and Taşkın Padır. My research interests lie at the intersection of remote sensing technologies and robotics. Currently, my proposed dissertation focuses on bringing hyperspectral imaging and near infrared spectroscopy into lighter, cheaper form factors for use in household robots. I am midway through a research thrust involving non-contact material estimation methods to infer object properties such as weight and friction. I welcome all collaborations on practical applications of hyperspectral imaging in real-time processing and vision systems that extend beyond the visible wavelengths of light.
Download my current CV.
- Robot Spectroscopy
- Hyperspectral Sensing
- Multi-Modal Sensing
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PhD Computer Engineering, 2023
Northeastern University
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MS Computer Science, 2020
Boston University
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BSc Computer Engineering, 2019
University of Notre Dame
Skills
Experience
My current research directions include:
- Development of novel gripper designs embedding visible to near infrared (VNIR) spectroscopic probes to acquire in-hand material readings
- Extending standard RGB machine vision into higher dimensional space via inclusion of visible and short wave infrared imaging systems
- Fusing point clouds with hyperspectral datacubes to create spatial-material representations of the world
At any given time, I mentor a handful of talented undergraduate and graduate students. I am our lab’s webmaster and resident tour guide!
Projects
Featured Publications
Terrain classification is a challenging task for robots operating in unstructured environments. Existing classification methods make simplifying assumptions, such as a reduced number of classes, clearly segmentable roads, or good lighting conditions, and focus primarily on one sensor type. These assumptions do not translate well to off-road vehicles, which operate in varying terrain conditions. To provide mobile robots with the capability to identify the terrain being traversed and avoid undesirable surface types, we propose a multimodal sensor suite capable of classifying different terrains. We capture high resolution macro images of surface texture, spectral reflectance curves, and localization data from a 9 degrees of freedom (DOF) inertial measurement unit (IMU) on 11 different terrains at different times of day. Using this dataset, we train individual neural networks on each of the modalities, and then combine their outputs in a fusion network. The fused network achieved an accuracy of 99.98% percent on the test set, exceeding the results of the best individual network component by 0.98%. We conclude that a combination of visual, spectral, and IMU data provides meaningful improvement over state of the art in terrain classification approaches. The data created for this research is available at https://github.com/RIVeR-Lab/vast_data.
Recent Publications
Contact
I am always looking for collaborators. Please reach out if you would like to work together!
- hanson [.] n [@] northeastern [dot] edu