15 citations · 28 across the 7 of their papers we have counts for
7 papers
Embody 3D: A Large-scale Multimodal Motion and Behavior Dataset
Claire McLean, Makenzie Meendering, Tristan Swartz +21
The Codec Avatars Lab at Meta introduces Embody 3D, a multimodal dataset of 500 individual hours of 3D motion data from 439 participants collected in a multi-camera collection stag…
BETTY Dataset: A Multi-modal Dataset for Full-Stack Autonomy
Micah Nye, Ayoub Raji, Andrew Saba +9
We present the BETTY dataset, a large-scale, multi-modal dataset collected on several autonomous racing vehicles, targeting supervised and self-supervised state estimation, dynamic…
Fast and Modular Autonomy Software for Autonomous Racing Vehicles
Andrew Saba, Aderotimi Adetunji, Adam Johnson +27
Autonomous motorsports aim to replicate the human racecar driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their han…
TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks
Matthew Sivaprakasam, Parv Maheshwari, Mateo Guaman Castro +6
We present TartanDrive 2.0, a large-scale off-road driving dataset for self-supervised learning tasks. In 2021 we released TartanDrive 1.0, which is one of the largest datasets for…
RACECAR -- The Dataset for High-Speed Autonomous Racing
Amar Kulkarni, John Chrosniak, Emory Ducote +6
This paper describes the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operati…
Robust Modeling and Controls for Racing on the Edge
Joshua Spisak, Andrew Saba, Nayana Suvarna +5
Race cars are routinely driven to the edge of their handling limits in dynamic scenarios well above 200mph. Similar challenges are posed in autonomous racing, where a software stac…
Mission-level Robustness with Rapidly-deployed, Autonomous Aerial Vehicles by Carnegie Mellon Team Tartan at MBZIRC 2020
Anish Bhattacharya, Akshit Gandhi, Lukas Merkle +7
For robotic systems to succeed in high risk, real-world situations, they have to be quickly deployable and robust to environmental changes, under-performing hardware, and mission s…