Human eyes can recognize an object just by looking at the environment; this capability is very useful for designing the reference of a humanoid robot with the ability for adapting to its environment. By knowing the field conditions that exist in such environments, the robot can understand the obstacles or anything that can be passed. To do that, robot's vision needs to has the knowledge to understand an obstacle that exists around it. Because of these problems, this paper shows a method for reducing the error rate of vacant space in the data depth by combining a stereo camera and structure sensor. Merging the stereo camera and structure sensor can extract depth information becomes dense. The proposed method has been successfully running the whole algorithm that already built and has a density of depth with an average error rate of vacant space is 18.10%.
Human eyes can recognize an object just by looking at the environment; this capability is very useful for designing the reference of a humanoid robot with the ability for adapting to its environment. By knowing the field conditions that exist in such environments, the robot can understand the obstacles or anything that can be passed. To do that, robot's vision needs to has the knowledge to understand an obstacle that exists around it. Because of these problems, this paper shows a method for reducing the error rate of vacant space in the data depth by combining a stereo camera and structure sensor. Merging the stereo camera and structure sensor can extract depth information becomes dense. The proposed method has been successfully running the whole algorithm that already built and has a density of depth with an average error rate of vacant space is 18.10%.
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