Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This work describes an approach for using several levels of data fusion in the domain of autonomous off-road navigation.  are focusing on outdoor obstacle detection, and  present techniques that leverage on data fusion and machine learning for increasing the reliability of obstacle detection systems.  are combining color and infrared (IR) imagery with range information from a laser range finder.  show that in addition to fusing data at the pixel level, performing high level classifier fusion is beneficial in our domain. Our general approach is to use machine learning techniques for automatically deriving effective models of the classes of interest (obstacle and non-obstacle for example).  train classifiers on different subsets of the features  extract from our sensor suite and show how different classifier fusion schemes can be applied for obtaining a multiple classifier system that is more robust than any of the classifiers presented as input.  present experimental results  obtained on data collected with both the experimental unmanned vehicle (XUV) and a CMU developed robotic tractor.
['Cristian Dima', 'Nicolas Vandapel', 'Martial Hebert']
I am writing a research paper on a idea Classifier fusion for outdoor obstacle detection
[{"from": "human", "value": "I am writing a research paper on a idea Classifier fusion for outdoor obstacle detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This work describes an approach for using several levels of data fusion in the domain of autonomous off-road navigation.  are focusing on outdoor obstacle detection, and  present techniques that leverage on data fusion and machine learning for increasing the reliability of obstacle detection systems.  are combining color and infrared (IR) imagery with range information from a laser range finder.  show that in addition to fusing data at the pixel level, performing high level classifier fusion is beneficial in our domain. Our general approach is to use machine learning techniques for automatically deriving effective models of the classes of interest (obstacle and non-obstacle for example).  train classifiers on different subsets of the features  extract from our sensor suite and show how different classifier fusion schemes can be applied for obtaining a multiple classifier system that is more robust than any of the classifiers presented as input.  present experimental results  obtained on data collected with both the experimental unmanned vehicle (XUV) and a CMU developed robotic tractor."}]