期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 5, 期 2, 页码 1931-1938出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2020.2969927
关键词
Simulation and animation; computer vision for automation; object detection; segmentation and categorization
类别
资金
- Baidu Research
In Autonomous Driving (AD), detection and tracking of obstacles on the roads is a critical task. Deep-learning based methods using annotated LiDAR data have been the most widely adopted approach for this. Unfortunately, annotating 3D point cloud is a very challenging, time- and money-consuming task. In this letter, we propose a novel LiDAR simulator that augments real point cloud with synthetic obstacles (e.g., vehicles, pedestrians, and other movable objects). Unlike previous simulators that entirely rely on CG (Computer Graphics) models and game engines, our augmented simulator bypasses the requirement to create high-fidelity background CAD (Computer Aided Design) models. Instead, we can deploy a vehicle with a LiDAR scanner to sweep the street of interests to obtain the background points cloud, based on which annotated point cloud can be automatically generated. This scan-and-simulate capability makes our approach scalable and practical, ready for large-scale industrial applications. In this letter, we describe our simulator in detail, in particular the placement of obstacles that is critical for performance enhancement. We show that detectors with our simulated LiDAR point cloud alone can perform comparably (within two percentage points) with these trained with real data. Mixing real and simulated data can achieve over 95% accuracy.
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