Looming, traditionally defined as the relative expansion of objects in the observer's retina, is a fundamental visual cue for perception of threat and can be used to accomplish collision free navigation. The measurement of the looming cue is not only limited to vision, and can also be obtained from range sensors like LiDAR (Light Detection and Ranging). In this article we present two methods that process raw LiDAR data to estimate the looming cue. Using looming values we show how to obtain threat zones for collision avoidance tasks. The methods are general enough to be suitable for any six-degree-of-freedom motion and can be implemented in real-time without the need for fine matching, point-cloud registration, object classification or object segmentation. Quantitative results using the KITTI dataset shows advantages and limitations of the methods.
翻译:Looming传统上被定义为观察员视网膜中物体的相对扩展,是威胁感知的基本直观提示,可用于实现无碰撞航行。正在逼近的提示的测量不仅局限于视觉,还可以从LIDAR(光探测和测距)等射程传感器获得。在本篇文章中,我们介绍了两种处理LIDAR原始数据以估计逼近的提示的方法。我们使用隐蔽值来显示如何获得避免碰撞任务的威胁区。这些方法很笼统,足以适用于任何六度自由运动,可以实时实施,而不需要精细匹配、点宽登记、物体分类或物体分割。使用KITTI数据集的定量结果显示了方法的优点和局限性。