TH\"OR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset

TL;DR

TH"OR is a comprehensive human-robot navigation dataset that includes detailed motion trajectories, eye gaze, sensor data, and annotations, enabling advanced research in human behavior understanding and robot navigation.

Contribution

This paper introduces TH"OR, a new dataset with high-quality, diverse human motion and gaze data, along with sensor information and metrics for analysis, surpassing existing datasets in content and accuracy.

Findings

TH"OR dataset offers more diverse human motion behaviors.

It contains higher frequency annotations and less noise.

The dataset includes comprehensive sensor and environmental data.

Abstract

Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and predict human behavior as well as to plan and interact with humans has received increasing attention over the last years. The availability of large human motion datasets that contain relevant levels of difficulty is fundamental to this research. Existing datasets are often limited in terms of information content, annotation quality or variability of human behavior. In this paper, we present TH\"OR, a new dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. TH\"OR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. We propose a set of metrics to quantitatively analyze motion trajectory datasets such as the average tracking duration, ground truth noise, curvature and speed variation of the trajectories. In comparison to prior art, our dataset has a larger variety in human motion behavior, is less noisy, and contains annotations at higher frequencies.