Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation

Understand

Mobile robots and autonomous vehicles rely on multi-modal sensor setups to perceive and understand their surroundings.

• Aside from cameras, LiDAR sensors represent a central component of state-of-the-art perception systems.
• In addition to accurate spatial perception, a comprehensive semantic understanding of the environment is essential for efficient and safe operation.
• In this paper we present a novel deep neural network architecture called LiLaNet for point-wise, multi-class semantic labeling of semi-dense LiDAR data.