Daytime water detection based on color variation

Robust water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation. This is particularly true in wide open areas where water can collect in naturally occurring terrain depressions during periods of heavy precipitation and form large water bodies (such as ponds). At far range, reflections of the sky provide a strong cue for water. But at close range, the color coming out of a water body dominates sky reflections and the water cue from sky reflections is of marginal use. We model this behavior by using water body intensity data from multiple frames of RGB imagery to estimate the total reflection coefficient contribution from surface reflections and the combination of all other factors. We then describe an algorithm that uses one of the color cameras in a forward-looking, UGV-mounted stereo-vision perception system to detect water bodies in wide open areas. This detector exploits the knowledge that the change in saturation-to-brightness ratio across a water body from the leading to trailing edge is uniform and distinct from other terrain types. In test sequences approaching a pond under clear, overcast, and cloudy sky conditions, the true positive and false negative water detection rates were (95.76%, 96.71%, 98.77%) and (0.45%, 0.60%, 0.62%), respectively. This software has been integrated on an experimental unmanned vehicle and field tested at Ft. Indiantown Gap, PA, USA.

I. Introduction

Detecting water hazards is a significant challenge to unmanned ground vehicle (UGV) autonomous navigation over cross country terrain. This is particularly true for military UGVs navigating over wide open areas such as clearings. Here, higher vehicle speeds are desired when executing tactical behaviors such as “dash to cover”. But these are areas that typically contain some naturally occurring depressions where large water bodies (such as ponds) can form during periods of heavy precipitation. The probability of driving into a water hazard increases when a UGV is required to operate at higher than normal speeds, especially since a priori water data may not be available as current water hazards may not have existed when the most recent digital terrain map was generated. Thus, the need to traverse wide open areas at high speed while detecting and avoiding previously unknown water traps necessitates robust water detection.