About
Hello! I'm Derek, a PhD student in the Field Robotics Lab at Brigham Young University. I love working on hands on projects where I can play a part in the development of multiple aspects in a robotic system. I hope to graduate winter 2025 or spring 2026 and am actively looking for internship opportunities for summer 2025.
Download My ResumeMy Skills
C/C++
Python
Embedded Systems
Perception and Navigation
Machine Learning
Kalman Filtering
SLAM
Computer Vision
Optimization
Research
Gaussian Process Based Mapping
Currently researching ways we can map and explore with Gaussian Process Regression to minimize map uncertainty in underwater environments
ASV State Estimation
I'm researching robust state estimators for autonomous surface vessels (ASVs). By fusing data from an Inertial Measurement Unit (IMU), GPS, and a monocular camera capturing the horizon, I aim to estimate the complete state of our WAM-V 8 ASV. This will enable the creation of accurate seafloor maps even in challenging ocean conditions.
PUBLICATIONS
Low-Cost Urban Localization with Magnetometer and LoRa Technology
Derek Benham, Ashton Palacios, Philip Lundrigan, Joshua G. Mangelson
International Conference on Intelligent Robots and Systems 2024, Abu Dhabi, UAE
With the goal of developing low-cost and innovative perception and localization techniques for autonomous vehicles, this work explores a system that solely relies on a LoRa receiver and a magnetometer for agent localization within urban environments. Using the received signal strength from LoRa beacons distributed across a test area of 16,000 square meters, a model of expected RSSI values per beacon is estimated using Gaussian Process (GP) regression. Motion is estimated using a probabilistic signal similarity classifier, and localization is obtained via a particle filter. Our experiments demonstrate that our proposed system is able to estimate our location to within three meters RMSE. In real-world scenarios, characterized by signal interference and environmental complexities, our approach highlights the potential of leveraging affordable technology such as LoRa receivers and magnetometers for robust and accurate location estimation in complex urban environments. The integration of low-cost LoRa devices, Gaussian Process regression, particle filtering and our novel signal similarity motion estimator offers a promising avenue for achieving cost-effective localization solutions without compromising accuracy or reliability.
Manuscript Accepted, Under Final Preparation3D Reconstruction of Reefs using Autonomous Surface Vessels and an Analysis of Chain vs 3D Rugosity Measurement Robustness
Derek Benham, Aaron Newman, Kalai Ellis, Richard Gill, and Joshua G. Mangelson
IEEE/OES Oceans Conference 2022, Hampton Roads, VA
Coral reefs are at risk. To study and minimize the impacts of global warming, pollution, or land sediment disposition on the reef, regular and accurate measurements are needed to assess the coral's health. We present a method of using surface vessels to autonomously collect GPS-tagged images to be used in creating a 3D model of the reef which we tested in Molokai, Hawaii. We also discuss the shortcomings of chain rugosity measurements (the longtime standard for categorizing reef health) and how surface complexity measurements, a metric only obtained from creating 3D models from imagery are less subject to these flaws.
Full PaperRecent Projects
