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For reliable navigation, a mobile robot needs to be able to recognize where it is in the world. We describe an efficient and effective image-based representation of perceptual information for place recognition. Each place is associated with a set of stored image signatures, each a matrix of numbers derived by evaluating some measurement function over large blocks of pixels. Measurements are chosen to be characteristic of a location yet reasonably invariant over different viewing conditions. Signature matching can be done quickly by element wise comparison. Additional stability can be gotten by matching signatures at offsets or across scales. Signatures can be stored in a k-d tree so that retrieval of similar signatures is fast. We can also use several types of measurements in tandem to enhance recognition accuracy. We present preliminary experimental results which show up to 90% recognition accuracy. When used together with prior position information, we suggest that this performance is good enough to support reliable place recognition from a series of images.
Engelson et al. (Thu,) studied this question.