Researchers’ new technique, which enhances conventional machine vision and perception, is improving the field of robotics and autonomy. They have created heat-assisted detection and ranging, or HADAR.
Conventional active sensors that gather three-dimensional data, such as LiDAR, radar, and sonar, have disadvantages such as interference and potential eye safety hazards. Conventional thermal imaging is a passive sensing technique that gathers intangible heat radiation from objects. However, it has drawbacks, such as the “ghosting effect,” which produces featureless, textureless images and complicates machine perception of heat radiation.
Heat-assisted detection and ranging, or HADAR, integrates thermal physics, infrared imaging, and machine learning to enable totally passive and physics-aware machine perception.
The paper demonstrates that complete darkness conveys the same amount of information as bright daylight by building on the information-theoretic underpinnings of thermal perception. Humans have evolved to be skewed toward the day. It will no longer be possible for machines to distinguish between day and night in the future.
HADAR reliably disentangles the temperature, emissivity, and texture (TeX) of all objects in an image and vividly recovers the texture from the congested heat signal. Beyond RGB, red, green, and blue, visual imaging, or traditional thermal sensing, it recognizes physical qualities and sees texture and depth through the darkness as if it were daytime. The ability to see into complete darkness as clearly as daylight is astounding. The group used an off-road, nocturnal setting to evaluate HADAR TeX vision.
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