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  • 1. Key Characteristics of Data Fuel
  • 1.1. Controllable Data Generation
  • 1.2. Verifiable Provenance
  • 1.3. Multimodal & Semantic Alignment
  • 2. Ongoing Collaborations and Application Validation
  • 2.1. HD Maps and Urban Spatial Data Management
  • 2.2. Controllable Data Generation for Autonomous Driving
  • 2.3. Human-Centric Data Collection for Humanoid Robot Training
  • 3. LightCone Batch-2 Waitlist Now Open
  • Contact Us
  1. Journey into ROVR

Journey into ROVR - Pioneering Controllable Data Generation for Robotics and AI

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Last updated 10 days ago

Within 6 weeks, 45 activated units, 140k+ km high-precision LiDAR and image data. All data has been structured, encrypted, and uploaded into the ROVR 3D data asset network. This milestone marks ROVR Network’s transition into operational, controllable data generation — providing high-precision, trustworthy 3D data from the physical world to serve autonomous driving, robotic navigation, and generative AI applications.

1. Key Characteristics of Data Fuel

As foundation models enter the era of spatial intelligence, their understanding of the physical 3D world still heavily relies on simulated environments or closed-source datasets. ROVR LightCone offers developers a new paradigm of real-world data acquisition — built for openness, precision, and scale.

1.1. Controllable Data Generation

  • Users can precisely define the target environment: urban areas, highways, industrial zones, or rural regions

  • All data is captured in a consistent and structured format: LiDAR point clouds, camera images, RTK GPS, IMU, and calibrated sensor parameters

1.2. Verifiable Provenance

  • Every data segment includes cryptographically verifiable metadata: capture location, time, GPS precision, and upload timestamps

  • Each dataset is traceable to a specific device and user

  • End-to-end encrypted signatures and on-chain indexing ensure data integrity and auditability

1.3. Multimodal & Semantic Alignment

  • LiDAR and image streams are temporally and spatially aligned during capture

  • AI pipelines enable the generation of semantic labels for 3D instance segmentation, BEV detection, SLAM, and reconstruction

  • Output available in ROSBag format, convertible into nuScenes, KITTI, Waymo Open Dataset, and other standard formats

2. Ongoing Collaborations and Application Validation

2.1. HD Maps and Urban Spatial Data Management

  • Autonomous Driving HD Map Construction:

    • Generation of HD maps containing static elements such as lane markings, curbs, and traffic signs

    • Supports specification alignment and change detection workflows

    • Meets precision requirements for Level 3–4 autonomous driving applications

  • Traditional Surveying and Urban Spatial Data Acquisition

    • Supports municipal asset inventory, road and bridge structure modeling, and spatial data collection of transportation infrastructure such as clearance bars

    • Enables fine-grained urban management and planning through high-resolution 3D spatial data

2.2. Controllable Data Generation for Autonomous Driving

  • Applied to the Perception model Training and Simulation for Autonomous Driving

Using technologies such as 3D Gaussian Splatting (3DGS) and NeRF, raw LightCone data can be transformed into customized virtual scenes — including configurable camera setups and controllable scene variations.

The following demo showcases a scenario where all vehicles have been replaced with a single vehicle type, demonstrating editable semantics for synthetic training.

  • Applied to Learning-based Planning and Control (PnC) Model Training Using Reinforcement Learning and Generative AI

By leveraging real-world data collected from LightCone and generative models within the ROVR Network, the ego-vehicle’s trajectory can be modified to produce alternative driving behaviors.

In the following demo, lane-change data is synthetically generated from straight-driving sequences. This approach enables autonomous driving companies to train and simulate their planning and control (PnC) modules more efficiently.

2.3. Human-Centric Data Collection for Humanoid Robot Training

  • By deploying LightCone in diverse indoor and outdoor environments, structured 3D point cloud and image data can be captured to support the training of models for humanoid robot navigation, obstacle avoidance, motion planning, and scene understanding.

  • The system supports data collection at varying heights and perspectives to simulate human-like perception, providing high-density, multimodal, and controllably labeled datasets — ideal for pretraining next-generation general-purpose robotics foundation models.


3. LightCone Batch-2 Waitlist Now Open

Contact Us

If you are interested in purchasing the ROVR LightCone device or would like to learn more about the technical details, feel free to contact us. Our team will provide you with comprehensive technical support and services.

Thank you!

ROVR Team

To meet the growing demand from data customers and expand the coverage and frequency of data collection, we plan to launch the second batch of LightCone pre-sales in mid-June, with deliveries expected to begin in September. Register now for the to secure your priority purchase opportunity!

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