To implement autonomous driving, one essential step is to model the vehicle
environment based on the sensor inputs. Radars, with their well-known
advantages, became a popular option to infer the occupancy state of grid cells
surrounding the vehicle. To tackle data sparsity and noise of
本文提出一种基于深度学习的 RaLL 框架,将雷达和激光雷达嵌入到共同的神经网络特征空间中,利用激光雷达现成的映射技术来实现雷达在室外环境下的精准定位和低成本的雷达地图构建。实验结果表明,该系统在 90km 的驾车中具有优异的性能,甚至在 UK 训练,South Korea 测试的泛化场景中仍然表现出色。