Simulating LIDAR Point Cloud for Autonomous Driving using Real-world Scenes and Traffic Flows

In this paper, we describe our simulator in detail, in particular the placement of obstacles that is critical for performance enhancement. This unique ”scan-and-simulate” capability makes our approach scalable and practical, ready for large-scale industrial applications. Instead, we can simply deploy a vehicle with a LiDAR scanner to sweep the street of interests to obtain the background point cloud, based on which annotated point cloud can be automatically generated.

Unlike previous simulators that entirely rely on CG models and game engines, our augmented simulator bypasses the requirement to create high-fidelity background CAD models. In this paper, we propose a novel LiDAR simulator that augments real point cloud with synthetic obstacles (e.g., cars, pedestrians, and other movable objects).

Unfortunately, annotating 3D point cloud is a very challenging, time- and money-consuming task.

Deep-learning based methods using annotated LiDAR data have been the most widely adopted approach for this. In Autonomous Driving (AD), detection and tracking of obstacles on the roads is a critical task.

Simulating LIDAR Point Cloud for Autonomous Driving using Real-world Scenes and Traffic Flows