Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describe results on combining depth information from a laser range-finder and color and texture image cues to segment ill-structured dirt, gravel, and asphalt roads as input to an autonomous road following system. A large number of registered laser and camera images re captured at frame-rate on a variety,of rural roads, allowing laser features such as 3-D height and smoothness to be correlated with image features such as color histograms and Gabor filter responses. A small set of road models was generated by training separate neural networks on labeled feature vectors clustered by road "type." By first classifying the type of a novel road image, an appropriate second-stage classifier was selected to segment individual pixels, achieving a high degree of accuracy on arbitrary images from the dataset. Segmented images combined with laser range information and the vehicle's inertial navigation data re used to construct 3-D maps suitable for path planning.
['Christopher Rasmussen']
I am writing a research paper on a idea Combining laser range, color, and texture cues for autonomous road following
[{"from": "human", "value": "I am writing a research paper on a idea Combining laser range, color, and texture cues for autonomous road following"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describe results on combining depth information from a laser range-finder and color and texture image cues to segment ill-structured dirt, gravel, and asphalt roads as input to an autonomous road following system. A large number of registered laser and camera images re captured at frame-rate on a variety,of rural roads, allowing laser features such as 3-D height and smoothness to be correlated with image features such as color histograms and Gabor filter responses. A small set of road models was generated by training separate neural networks on labeled feature vectors clustered by road \"type.\" By first classifying the type of a novel road image, an appropriate second-stage classifier was selected to segment individual pixels, achieving a high degree of accuracy on arbitrary images from the dataset. Segmented images combined with laser range information and the vehicle's inertial navigation data re used to construct 3-D maps suitable for path planning."}]