TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation

TL;DR

TartanGround is a comprehensive large-scale dataset designed to enhance perception and navigation capabilities of ground robots in diverse environments, supporting various learning-based tasks and addressing generalization challenges.

Contribution

It introduces a multi-modal, photorealistic dataset with extensive trajectories and labels, enabling robust training and evaluation of robotic perception models in diverse scenarios.

Findings

State-of-the-art methods struggle to generalize across diverse scenes.

TartanGround enables training for multiple perception and navigation tasks.

The dataset supports advancing robotic autonomy in varied environments.

Abstract

We present TartanGround, a large-scale, multi-modal dataset to advance the perception and autonomy of ground robots operating in diverse environments. This dataset, collected in various photorealistic simulation environments includes multiple RGB stereo cameras for 360-degree coverage, along with depth, optical flow, stereo disparity, LiDAR point clouds, ground truth poses, semantic segmented images, and occupancy maps with semantic labels. Data is collected using an integrated automatic pipeline, which generates trajectories mimicking the motion patterns of various ground robot platforms, including wheeled and legged robots. We collect 910 trajectories across 70 environments, resulting in 1.5 million samples. Evaluations on occupancy prediction and SLAM tasks reveal that state-of-the-art methods trained on existing datasets struggle to generalize across diverse scenes. TartanGround can serve as a testbed for training and evaluation of a broad range of learning-based tasks, including occupancy prediction, SLAM, neural scene representation, perception-based navigation, and more, enabling advancements in robotic perception and autonomy towards achieving robust models generalizable to more diverse scenarios. The dataset and codebase are available on the webpage: https://tartanair.org/tartanground