A Hierarchical Approach to Enhance Accuracy of Place Recognition
A. Nayak
An intelligent autonomous navigation system must be able to accurately locate itself and recognize already visited places (loop-closure detection). In this regard, recent researches have put a lot of emphasis on appearance-based mapping. Kazmi et al. (2019) proposed a novel method which combines modified growing self-organizing maps (GSOM) with Bayesian framework to learn the representation of the environment incrementally using gist features. Gist features are global descriptor which are a single vector representation of an image. Images which are nearby and share similar global features are associated to a single entity called neuron. However, due to perceptual ambiguity, places which are far apart may appear similar and are mapped to a single neuron causing false detection of loop closure. In this master’s thesis, we aim to build upon Kazmi et al. (2019) and improve the accuracy of place recognition (loop-closure) by using a hierarchical approach. To this end, local features in images have been used to retrieve the best match from a set of places mapped to a neuron by using geometric validation. A set of methods are formulated to improve accuracy using local descriptors and robust error estimation methods and data sets are used to evaluate the system’s performance. The robustness of the proposed approach, in presence of perceptual ambiguity, is demonstrated by experimental results, where different evaluation measures like RANSAC, etc. are used. Various feature detector-descriptor combination are evaluated to find efficient ways of minimizing false detection.