论文标题
迈向自主驾驶：多模式360 $^{\ CIRC} $感知提案
Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal
论文作者
论文摘要
在本文中，介绍了用于3D对象检测和自动驾驶汽车跟踪的多模式360 $^{\ circ} $框架。该过程分为四个主要阶段。首先，将图像馈入CNN网络，以获取周围路参与者的实例分割。其次，对估计的面具建议进行激光射线到图像的关联。然后，每个对象的隔离点由点网集处理以计算其相应的3D边界框和姿势。最后，使用基于无知的卡尔曼滤波器的跟踪阶段用于随着时间的推移跟踪代理。该解决方案基于新型的传感器融合配置，可提供准确可靠的道路环境检测。部署在自动驾驶汽车中的系统的各种测试成功地评估了拟议的感知堆栈在真正的自动驾驶应用中的适用性。
In this paper, a multi-modal 360$^{\circ}$ framework for 3D object detection and tracking for autonomous vehicles is presented. The process is divided into four main stages. First, images are fed into a CNN network to obtain instance segmentation of the surrounding road participants. Second, LiDAR-to-image association is performed for the estimated mask proposals. Then, the isolated points of every object are processed by a PointNet ensemble to compute their corresponding 3D bounding boxes and poses. Lastly, a tracking stage based on Unscented Kalman Filter is used to track the agents along time. The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection. A wide variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack in a real autonomous driving application.
