THUD++: Large-Scale Dynamic Indoor Scene Dataset and Benchmark for Mobile Robots

TL;DR

THUD++ is a comprehensive large-scale indoor dataset with real and synthetic dynamic scenes, designed to improve the evaluation and development of mobile robot algorithms in complex, crowded, and changing environments.

Contribution

The paper introduces THUD++, a new large-scale dynamic indoor scene dataset with diverse scenarios, real and synthetic data, and a simulation platform, filling a gap in existing robotic datasets.

Findings

State-of-the-art methods face challenges in dynamic indoor environments.

The dataset enables benchmarking across multiple scene understanding tasks.

Experiments reveal key difficulties in navigation and perception in crowded scenes.

Abstract

Most existing mobile robotic datasets primarily capture static scenes, limiting their utility for evaluating robotic performance in dynamic environments. To address this, we present a mobile robot oriented large-scale indoor dataset, denoted as THUD++ (TsingHua University Dynamic) robotic dataset, for dynamic scene understanding. Our current dataset includes 13 large-scale dynamic scenarios, combining both real-world and synthetic data collected with a real robot platform and a physical simulation platform, respectively. The RGB-D dataset comprises over 90K image frames, 20M 2D/3D bounding boxes of static and dynamic objects, camera poses, and IMU. The trajectory dataset covers over 6,000 pedestrian trajectories in indoor scenes. Additionally, the dataset is augmented with a Unity3D-based simulation platform, allowing researchers to create custom scenes and test algorithms in a controlled environment. We evaluate state-of-the-art methods on THUD++ across mainstream indoor scene understanding tasks, e.g., 3D object detection, semantic segmentation, relocalization, pedestrian trajectory prediction, and navigation. Our experiments highlight the challenges mobile robots encounter in indoor environments, especially when navigating in complex, crowded, and dynamic scenes. By sharing this dataset, we aim to accelerate the development and testing of mobile robot algorithms, contributing to real-world robotic applications.