It is a significant problem to predict the 2D LiDAR map at next moment for robotics navigation and path-planning. To tackle this problem, we resort to the motion flow between adjacent maps, as motion flow is a powerful tool to process and analyze the dynamic data, which is named optical flow in video processing. However, unlike video, which contains abundant visual features in each frame, a 2D LiDAR map lacks distinctive local features. To alleviate this challenge, we propose to estimate the motion flow based on deep neural networks inspired by its powerful representation learning ability in estimating the optical flow of the video. To this end, we design a recurrent neural network based on gated recurrent unit, which is named LiDAR-FlowNet. As a recurrent neural network can encode the temporal dynamic information, our LiDAR-FlowNet can estimate motion flow between the current map and the unknown next map only from the current frame and previous frames. A self-supervised strategy is further designed to train the LiDAR-FlowNet model effectively, while no training data need to be manually annotated. With the estimated motion flow, it is straightforward to predict the 2D LiDAR map at the next moment. Experimental results verify the effectiveness of our LiDAR-FlowNet as well as the proposed training strategy. The results of the predicted LiDAR map also show the advantages of our motion flow based method.
翻译:对于机器人导航和路径规划而言,下一个时刻预测 2D LiDAR 地图是一个重大问题。 为了解决这个问题,我们使用相邻地图之间的运动流,因为运动流是处理和分析动态数据的有力工具,在视频处理过程中称为光学流。然而,与视频不同,2D LiDAR 地图在每一个框架中都包含丰富的视觉特征,2D LiDAR 地图缺乏独特的本地特征。为了减轻这一挑战,我们提议根据深度神经网络的强大代表性学习能力来估计运动流,以估计视频光学流。为此,我们设计了一个以Gated 经常性单元(名为LiDAR-FlowNet)为基础的经常性神经网络。由于一个经常性的神经网络可以编码时间动态信息,我们的LID-FlowNet可以估计当前地图和未知的下一个地图之间的运动流,只有当前框架和以前的框架才有不同的地方特征。为了有效培训LDAR-F 模型模型,而无需对培训数据进行手动说明。为此,我们设计了一个经常性的神经网络网络网络网络。由于一个经常性的神经网络网络网络可以对时间动态信息进行编码,因此可以直接地预测我们的LiD 培训结果,从而预测我们的LiAR 。