Water bodies are challenging terrain hazards for terrestrial unmanned ground vehicles (UGVs) for several reasons. Traversing through deep water bodies could cause costly damage to the electronics of UGVs. Additionally, a UGV that is either broken down due to water damage or becomes stuck in a water body during an autonomous operation will require rescue, potentially drawing critical resources away from the primary operation and increasing the operation cost. Thus, robust water detection is a critical perception requirement for UGV autonomous navigation.
Stereo Range Data may be a water body still too small to be detected in image space. In this example, the hole was labeled a potential hazard in the world map in frame N. In the next frame, where there was previously a hole, there was range data that was detected as an object reflection, providing confirmation of a water body." class="caption" align="right">One of the properties useful for detecting still water bodies is that their surface acts as a horizontal mirror at high incidence angles. Still water bodies in wide-open areas can be detected by geometrically locating the exact pixels in the sky that are reflecting on candidate water pixels on the ground, predicting if ground pixels are water based on color similarity to the sky and local terrain features. But in cluttered areas where reflections of objects in the background dominate the appearance of the surface of still water bodies, detection based on sky reflections is of marginal value. Specifically, this software attempts to solve the problem of detecting still water bodies on crosscountry terrain in cluttered areas at low cost.
Still water bodies are indirectly detected in cluttered areas of crosscountry terrain by detecting reflections of objects in the water bodies using imagery acquired from a stereo pair of color cameras, which are mounted to the front of a terrestrial UGV. Object reflections can be from naturally occurring (e.g. vegetation, trees, hills, mountains, clouds) or man-made entities (e.g. signs, poles, vehicles, buildings, bridges). Color cameras provide a lower-cost solution than specialized imaging sensors (such as a polarization camera) and laser scanners. In addition, object reflections can be detected in water bodies with stereo vision at further ranges than with lidar scanners.
Four methods for detecting object reflections have been implemented: detection in the rectified camera images using cross correlation, detection in stereo range images, detection in a world map generated from range data, and detection using combined stereo range images and rectified camera images. Detection in stereo range images (see figure) exploits the knowledge that 3D coordinates of stereo range data on object reflections occur below the ground surface at a range close to that of the reflecting object.
Any autonomous robotic platform used on cross-country terrain that has restrictions on driving through water could benefit from this software, including military platforms and perhaps some agricultural platforms. The automotive industry could potentially benefit from an application of this technology to detect wet pavement.
This work was done by Arturo L. Rankin and Larry H. Matthies of Caltech for NASA’s Jet Propulsion Laboratory.
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