Mobile Robot Oriented Large-Scale Indoor Dataset for Dynamic Scene Understanding

TL;DR

This paper introduces the THUD dataset, a large-scale indoor dataset with real and synthetic dynamic scenes, to improve evaluation and development of mobile robot scene understanding algorithms in complex, crowded environments.

Contribution

The paper presents a new large-scale dynamic indoor dataset for mobile robots, including detailed data collection, annotation, and evaluation of scene understanding tasks.

Findings

Challenges in dynamic scene understanding for robots.

Dataset enables evaluation of 3D detection, segmentation, and relocalization.

Supports development of algorithms for real-world dynamic environments.

Abstract

Most existing robotic datasets capture static scene data and thus are limited in evaluating robots' dynamic performance. To address this, we present a mobile robot oriented large-scale indoor dataset, denoted as THUD (Tsinghua University Dynamic) robotic dataset, for training and evaluating their dynamic scene understanding algorithms. Specifically, the THUD dataset construction is first detailed, including organization, acquisition, and annotation methods. It comprises both real-world and synthetic data, collected with a real robot platform and a physical simulation platform, respectively. Our current dataset includes 13 larges-scale dynamic scenarios, 90K image frames, 20M 2D/3D bounding boxes of static and dynamic objects, camera poses, and IMU. The dataset is still continuously expanding. Then, the performance of mainstream indoor scene understanding tasks, e.g. 3D object detection, semantic segmentation, and robot relocalization, is evaluated on our THUD dataset. These experiments reveal serious challenges for some robot scene understanding tasks in dynamic scenes. By sharing this dataset, we aim to foster and iterate new mobile robot algorithms quickly for robot actual working dynamic environment, i.e. complex crowded dynamic scenes.