Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a probabilistic approach to the problem of recognizing places based on their appearance. The system  present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment—identical but indistinctive observations receive a low probability of having come from the same place.  achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map, and is particularly suitable for online loop closure detection in mobile robotics.
['Mark Cummins', 'Paul Newman']
I am writing a research paper on a idea FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
[{"from": "human", "value": "I am writing a research paper on a idea FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a probabilistic approach to the problem of recognizing places based on their appearance. The system  present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment\u2014identical but indistinctive observations receive a low probability of having come from the same place.  achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map, and is particularly suitable for online loop closure detection in mobile robotics."}]