  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.
