ROVR Releases Open Dataset to Power the Future of Spatial AI, Robotics, and Autonomous Systems

Santa Clara, California, August 26th, 2025, Chainwire

At the ADAS & Autonomous Vehicle Technology Summit North America, ROVR, a leading decentralized physical infrastructure network (DePIN) building the foundation of Spatial AI, proudly announced the launch of the ROVR Open Dataset — a high-resolution, multi-modal dataset designed to accelerate innovation in Spatial AI, autonomous driving, robotics, and digital twin applications.

This release at one of the industry’s premier gatherings highlights ROVR’s commitment to supporting the autonomous vehicle ecosystem with open, high-fidelity data to fuel the next generation of intelligent mobility solutions.

The dataset marks a significant milestone in ROVR’s mission to democratize access to high-quality real-world data and unlock the next generation of AI models that understand and interact with physical space.

A Human-Centric View of the World

Unlike traditional datasets focused purely on machine vision, the ROVR Open Dataset captures the world as seen by human drivers — including what they see, how they move, and how they interact with their surroundings.

Collected using ROVR’s custom-built mobile perception units — operated by a global network of contributors — the dataset is part of a long-term effort to build the world’s largest open-access driving dataset, with a target of 1 million 30-second clips.

Each clip contains:

Raw LiDAR point clouds for detailed 3D spatial reconstruction
High-resolution RGB video from front-facing dashcams
High-frequency IMU data capturing motion dynamics
Centimeter-level RTK GPS localization for precise ground-truth positioning
Anonymized scenes for privacy-preserving and ethical AI development

The initial open release includes 1,500 fully synchronized clips, totaling more than 1TB of data. These clips offer diverse coverage across urban, suburban, and highway environments — including construction zones, school crossings, traffic congestion, and dynamic pedestrian scenes.

Beyond raw sensor data, ROVR is also building a scalable annotation pipeline for semantic segmentation, object detection, scene understanding, and intent prediction — enabling researchers and engineers to train next-generation foundation models for Spatial AI.

Future versions of the dataset will include: