TartanDrive dataset likely largest for off-road environments
Researchers from Carnegie Mellon University took an all-terrain vehicle on wild rides through tall grass, loose gravel and mud to gather data about how the ATV interacted with a challenging off-road environment.
They drove the heavily instrumented ATV aggressively at speeds up to 30 miles per hour. They slid through turns, took it up and down hills, and even got it stuck in the mud — all while gathering data such as video, the speed of each wheel and the amount of suspension shock travel from seven types of sensors.
The resulting dataset, called TartanDrive, includes about 200,000 of these interactions. The researchers believe the data is the largest real-world, multimodal, off-road driving dataset, both in terms of the number of interactions and types of sensors. The five hours of data could be useful for training a self-driving vehicle to navigate off road.
"Unlike autonomous street driving, off-road driving is more challenging because you have to understand the dynamics of the terrain in order to drive safely and to drive faster," said Wenshan Wang, a project scientist in the Robotics Institute (RI).
