Scale AI > Case Studies > Velodyne's Use of Scale Nucleus for Efficient Data Annotation in 3D Lidar Technology

Velodyne's Use of Scale Nucleus for Efficient Data Annotation in 3D Lidar Technology

Technology Category

Sensors - Autonomous Driving Sensors
Sensors - Lidar & Lazer Scanners

Applicable Industries

Automotive
E-Commerce

Applicable Functions

Warehouse & Inventory Management

Use Cases

Object Detection
Virtual Prototyping & Product Testing

Services

Cloud Planning, Design & Implementation Services
Training

About The Customer

Velodyne Lidar is a company that builds lidar sensors to enable safe navigation and autonomy. Their revolutionary sensor and software solutions cater to a wide range of industries, including robotics, industrial, infrastructure, and automotive. Velodyne’s lidars generate a high-quality point cloud in a wide variety of light and weather conditions, with advanced sensor-to-sensor interference mitigation, power efficiency, and thermal performance. Their customers, including e-commerce retailers and sidewalk robotics programs, use Velodyne's lidar-equipped robotics for accurate object detection, classification, and path estimation in fulfillment, delivery, and data-center operations.

The Challenge

Velodyne Lidar, a company that builds lidar sensors for safe navigation and autonomy across various industries, was facing a challenge in managing and selecting relevant training data from the large quantities of sensor data they collected. The data team found it relatively easy to classify common indoor robotics scenes as these scenarios made up a large portion of the datasets captured on their test robots. However, finding rarer scenarios, such as a warehouse employee stacking boxes on the top of a scissor lift, proved to be a difficult task. The team was in need of an out-of-the-box solution that could provide the necessary tools for efficient data selection and management.

The Solution

Velodyne Lidar adopted Scale Nucleus, a tool that allowed them to sort through edge cases in 3D sensor fusion data. The machine learning team captured image data along with the lidar point clouds and used the Object Autotag feature in Scale Nucleus to query, browse, and find similar images. These images were tied to 3D point clouds in a scene, enabling Velodyne to capture only point clouds of interest. Through point cloud scenes, Nucleus supported multimodal data, allowing the team to search 2D image data using the model zoo built into Nucleus, Object Autotag, and even natural language text queries to retrieve edge cases or exceptional examples in the associated 3D lidar data.

Operational Impact

With the implementation of Scale Nucleus, Velodyne's data team is now able to efficiently curate new datasets. The time taken to curate a 3D lidar dataset has been significantly reduced from days or even weeks to just a few hours. This has allowed the team to quickly identify new edge cases that their models are struggling with and refine ground truth as their library grows. More broadly, Velodyne's customers are using their lidar-equipped robotics for accurate object detection, classification, and path estimation, enabling them to move goods with a high level of precision, efficiency, and safety, and helping to maintain continuity of operations.

Quantitative Benefit

Nucleus reduced training data selection times for the data team by 50%.
The Velodyne team was able to drill down on edge cases in a matter of hours, reducing the dataset creation step and kicking off their annotation job days earlier.