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Thomas Sayre-McCord

Researcher at Massachusetts Institute of Technology

Publications -  10
Citations -  272

Thomas Sayre-McCord is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Trajectory & Robotics. The author has an hindex of 7, co-authored 9 publications receiving 189 citations. Previous affiliations of Thomas Sayre-McCord include Woods Hole Oceanographic Institution.

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Proceedings ArticleDOI

The WHOI Jetyak: An autonomous surface vehicle for oceanographic research in shallow or dangerous waters

TL;DR: The Woods Hole Oceanographic Institution Jetyak as mentioned in this paper is a small autonomous surface vehicle (ASV) designed for the collection of oceanographic data from shallow or dangerous waters, which is the result of custom modifications to a Mokai jet-powered kayak, including an A-frame and sea chest for installation of instrumentation.
Book ChapterDOI

The Blackbird Dataset: A Large-Scale Dataset for UAV Perception in Aggressive Flight

TL;DR: The Blackbird dataset as mentioned in this paper is a large-scale, aggressive indoor flight dataset collected using a custom-built quadrotor platform for use in evaluation of agile perception, which contains over 10 h of flight data from 168 flights over 17 flight trajectories and 5 environments at velocities up to 7.0 m.
Proceedings ArticleDOI

Visual-Inertial Navigation Algorithm Development Using Photorealistic Camera Simulation in the Loop

TL;DR: A new micro UAV platform that integrates high-rate cameras, inertial sensors, and an NVIDIA Jetson Tegra X1 system-on-chip compute module that boasts 256 GPU cores is presented and repeated agile maneuvering with closed-loop vision-based perception and control algorithms are demonstrated.
Journal ArticleDOI

The Blackbird UAV dataset

TL;DR: The Blackbird UAV dataset is well suited to the development of algorithms for visual inertial navigation, 3D reconstruction, and depth estimation, and as a benchmark for future algorithms, the performance of two state-of-the-art visual odometry algorithms are reported and scripts for comparing against the benchmarks are included with the dataset.