Estimation of Looming from LiDAR

TL;DR

This paper introduces two real-time LiDAR-based methods to estimate looming cues for collision avoidance, enhancing threat detection without complex processing like segmentation or registration.

Contribution

It presents novel LiDAR data processing techniques for looming estimation that are general, efficient, and do not require detailed object recognition.

Findings

Methods are suitable for any 6-DOF motion.

Quantitative results demonstrate effectiveness on KITTI dataset.

Approach enables threat zone detection for collision avoidance.

Abstract

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.