Abstract

3D road user detection is an essential task for autonomous vehicles and mobile robots, and it plays a key role, for instance, in obstacle avoidance and route planning tasks. Existing solutions for detection require expensive GPU units to run in real-time. This paper presents a light algorithm that runs in real-time without a GPU. The algorithm combines a classical point cloud proposal generator approach with a modern deep learning technique to achieve a small computational requirement and comparable accuracy to the state-of-the-art. Typical downsides of this approach, such as many out-of-distribution proposals and loss of location information, are examined, and solutions are proposed. We have evaluated the performance of the method with the KITTI dataset and with our own annotated dataset collected with a compact mobile robot platform equipped with a low-resolution LiDAR (16-channel). Our approach reaches a real-time inference on a standard CPU, unlike other solutions in the literature. Furthermore, we achieve superior speed on a GPU, which indicates that our method has a high degree of parallelism. Our method enables low-cost mobile robots to detect road users in real-time.

中文翻译：

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分布外和位置感知的 PointNet，无需 GPU 即可进行实时 3D 道路用户检测

摘要

3D 道路用户检测是自动驾驶汽车和移动机器人的一项重要任务，在避障和路线规划等任务中发挥着关键作用。现有的检测解决方案需要昂贵的 GPU 单元才能实时运行。本文提出了一种无需 GPU 即可实时运行的轻算法。该算法将经典的点云提案生成器方法与现代深度学习技术相结合，以实现较小的计算要求和与最先进技术相当的精度。研究了这种方法的典型缺点，例如许多分布外的提案和位置信息的丢失，并提出了解决方案。我们使用 KITTI 数据集和我们自己的带注释的数据集评估了该方法的性能，这些数据集是通过配备低分辨率 LiDAR（16 通道）的紧凑型移动机器人平台收集的。与文献中的其他解决方案不同，我们的方法可以在标准 CPU 上进行实时推理。此外，我们在 GPU 上实现了卓越的速度，这表明我们的方法具有高度的并行性。我们的方法使低成本移动机器人能够实时检测道路使用者。