Autonomous driving has achieved rapid development over the last few decades, including the machine perception as an important issue of it. Although object detection based on conventional cameras has achieved remarkable results in 2D/3D, non-visual sensors such as 3D LiDAR still have incomparable advantages in the accuracy of object position detection. However, the challenge also exists with the difficulty in properly interpreting point cloud generated by LiDAR. This paper presents a multi-modal-based online learning system for 3D LiDAR-based object classification in urban environments, including cars, cyclists and pedestrians. The proposed system aims to effectively transfer the mature detection capabilities based on visual sensors to the new model learning based on non-visual sensors through a multi-target tracker (i.e. using one sensor to train another). In particular, it integrates the Online Random Forests (ORF) [1] method, which inherently has the abilities of fast and multi-class learning. Through experiments, we show that our system is capable of learning a high-performance model for LiDAR-based 3D object classification on-the-fly, which is especially suitable for robotics in-situ deployment while responding to the widespread challenge of insufficient detector generalization capabilities.
翻译:在过去几十年中,自主驱动取得了迅速的发展,包括机器认为是其重要问题。虽然基于常规摄像机的物体探测在2D/3D方面取得了显著成果,但3D激光雷达等非视觉传感器在物体位置探测的准确性方面仍然具有无可比拟的优势;然而,由于难以正确解释LIDAR产生的点云,也存在挑战。本文为3D LiDAR基于3DAR的物体分类的城市环境,包括汽车、自行车手和行人提供了一个基于3DLIDAR的多式在线学习系统。拟议系统的目的是通过多目标跟踪器(即使用一个传感器培训另一个传感器),将基于视觉传感器的成熟探测能力有效转让给基于非视觉传感器的新模型学习。特别是,它整合了在线随机森林[1]方法,该方法本身具备快速和多级学习的能力。通过实验,我们显示我们的系统能够学习基于LDAR基于3D天体的物体飞行分类的高性能模型,该模型特别适合在普通测试中检测能力不足时,在普通测试中进行检测。