TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset

TL;DR

The TUM-VIE dataset offers a comprehensive collection of stereo event camera data, synchronized IMU and grayscale frames, and ground truth poses to advance 3D perception and navigation research in challenging environments.

Contribution

It introduces a large, diverse stereo event dataset with high-resolution sensors, synchronized multimodal data, and ground truth for evaluating visual-inertial perception algorithms.

Findings

Includes challenging sequences where current SLAM algorithms struggle.

Provides high-resolution stereo event data with synchronized IMU and frames.

Enables benchmarking and development of robust event-based perception methods.

Abstract

Event cameras are bio-inspired vision sensors which measure per pixel brightness changes. They offer numerous benefits over traditional, frame-based cameras, including low latency, high dynamic range, high temporal resolution and low power consumption. Thus, these sensors are suited for robotics and virtual reality applications. To foster the development of 3D perception and navigation algorithms with event cameras, we present the TUM-VIE dataset. It consists of a large variety of handheld and head-mounted sequences in indoor and outdoor environments, including rapid motion during sports and high dynamic range scenarios. The dataset contains stereo event data, stereo grayscale frames at 20Hz as well as IMU data at 200Hz. Timestamps between all sensors are synchronized in hardware. The event cameras contain a large sensor of 1280x720 pixels, which is significantly larger than the sensors used in existing stereo event datasets (at least by a factor of ten). We provide ground truth poses from a motion capture system at 120Hz during the beginning and end of each sequence, which can be used for trajectory evaluation. TUM-VIE includes challenging sequences where state-of-the art visual SLAM algorithms either fail or result in large drift. Hence, our dataset can help to push the boundary of future research on event-based visual-inertial perception algorithms.