nuScenes is an initiative intended to support research to further advance the mobility industry.
With this goal in mind, the dataset includes 1000 scenes collected in Boston and Singapore and is the largest multi-sensor dataset for autonomous vehicles.
Full sensor suite: 1x LiDAR, 5x RADAR, 6x camera, IMU, GPS
1000 scenes of 20s each
1,440,000 camera images
400,000 LiDAR sweepsl
Two diverse cities: Boston and Singapore
Left versus right hand traffic
For the dataset, Aptiv managed the collection of data, carefully choosing to capture challenging scenarios and a diversity of locations, times and weather conditions.
Collected in Boston's Seaport and Singapore's One North, Queenstown and Holland Village districts, each of the 1000 scenes in the dataset were manually selected.
Two identical cars with identical sensor layouts were used to drive in Boston and Singapore.
Refer to the image below for the placement of the sensors:
To achieve a high quality multi-sensor dataset, it is essential to calibrate the extrinsics and intrinsics of every sensor.
We express extrinsic coordinates relative to the ego frame, i.e. the midpoint of the rear vehicle axle.
The most relevant steps are described below:
Camera intrinsic calibration
In order to achieve cross-modality data alignment between the LiDAR and the cameras, the exposure on each camera was triggered when the top LiDAR sweeps across the center of the camera’s FOV. This method was selected as it generally yields good data alignment. Note that the cameras run at 12Hz while the LiDAR runs at 20Hz.
The 12 camera exposures are spread as evenly as possible across the 20 LiDAR scans, so not all LiDAR scans have a correspondingcamera frame.
Reducing the frame rate of the cameras to 12Hz helps to reduce the compute, bandwidth and storage requirement of the perception system.
As Aptiv's partner in developing nuScenes, Scale contributed two things: First, is the data annotation, including deciding on the taxonomy and developing instructions for labelers and managing QA.
Second is Scale's web-based visualizer for LiDAR and camera data for exploring the dataset. Scale's visualizer allows point cloud data to be easily embedded into any webpage and shared.
Ready to get started with nuScenes?
This tutorial will give you an overview of the dataset without the need to download it.
Please note that this page is a rendered version of a Jupyter Notebook.Take the Tutorial