Estimation of Looming from LiDAR
This work addresses collision avoidance for autonomous systems by providing a general method for threat detection, though it appears incremental as it adapts a known visual cue to LiDAR sensors.
The paper tackles the problem of estimating looming cues from raw LiDAR data for collision avoidance, presenting two methods that can be implemented in real-time without complex processing, with quantitative results evaluated on the KITTI dataset.
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.