Scale API has launched Sensor Fusion Annotation API for LiDAR and RADAR point cloud data, which is said to accelerate the development of perception algorithms for autonomous vehicles (AVs). The company leverages machine learning, statistical models and human-generated data to deliver accurate object recognition, capable of analyzing millions of camera images, LiDAR frames, and RADAR data each month. The combination of human and artificial intelligence results in rigorously tested training data that help autonomous vehicles more quickly learn to navigate independently while accurately identifying road markers, vehicles, and other objects in an instant.
Developers can leverage the Sensor Fusion and Image Annotation APIs to tap into several types of annotations:
- LiDAR/RADAR Annotation: Identifies objects in a 3D point cloud and draws bounding cuboids around the specified objects, returning the positions and sizes of these boxes.
- Semantic Segmentation: Classifies every pixel of an image according to the labels provided to return a full semantic, pixel-wise, and dense segmentation of the image.
- Polygon Annotation: Identifies objects (such as vehicles, pedestrians, cyclists, and more) and draws bounding polygons around the specified objects, returning the vertices of these polygons.
- Bounding Box Annotation: Identifies objects and draws bounding 2D boxes around the specified objects, returning the vertices of these boxes.
- Line Annotation: Identifies the different features of a road, such as lane lines, and draws segmented lines along each object, returning the vertices of these segmented lines.
- Point Annotation: Identifies the location of objects and draws points at specified locations, returning the locations of these points.
- Cuboid Annotation: Identifies objects and draws perspective 3D cuboids around the specified objects in camera images, returning the positions and sizes of these boxes.
The company’s comprehensive training data and advanced annotation tools will give manufacturers the tools they need to meet the demands of the 380 million self-driving cars projected to be on the road by 2030. Ready to sit back and leave the driving to whatever? More enlightenment is available for the Sensor Fusion Annotation API for LiDAR and RADAR.