AskReddit: Help with a guidance for my graduation thesis • /r/MachineLearning

Hello, I'm a computer scientist student, I will finish CS this year so I already started my graduation thesis. I work on a Computer Vision - Robotics lab here on my university and my main field of interest and that I want to pursue as an academic field is machine learning / deep learning, so I thought about mixing robotics with machine learning which is something very common. My main idea is Outdoor Autonomous Navigation, I want my robot to know what a grass is, what a tree is, what people and cars are so he can avoid it or do the things I will set it to do, my approach to the problem so far and what I already did is: For every image frame I slice the image into subImages and for each subImage I calculate it's histogram and compare with a huge data base containing tons of histograms of grass/sky/trees (for example) and run a knn/svm to classify the subImage into one of the closest histograms, and if everything goes by the script I will have a full labeled system for the robot, but I'm facing some problems and I'm not a really expert on the field yet so I really wan't some guidance because I don't know what to do, my professor told me this will be kinda hard to do this way and for a graduation thesis, I have implemented a LBP descriptor to classificate some textures like grass and asphalt but I can't use LBP for everything, I don't even know if the LBP will be accurate for grass and asphalt (if my dataset is huge enough), anyways, sorry for the long text, I just don't know what path to seek now, I don't even know if my current approach is a good one or I'm doing something silly.