The problem of localization is central to endowing mobile machines with intelligence. Vision is a promising research path because of its versatility and robustness in most unconstrained environments, both indoors and outdoors. Today, with many available studies in human vision, there is a unique opportunity to develop systems that take inspiration from neuroscience. In this work we examine several important issues on how the human brain deal with vision in general, and localization in particular.
For one, the human visual system extracts a plethora of features from different domains (for example: colors, orientations, intensity). Each of them brings a different perspective in scene understanding and allows humans to localize in many types of environment. Furthermore, the human brain also introduces multiple scene abstractions that complement each other. Here, we focus on the gist model, which rapidly summarizes a scene (general semantic classifications, spatial layout, etc.), and saliency model, which guides visual attention to specific conspicuous regions within the field of view.
One hallmark biological characteristic that we rely upon is the utilization of coarse-to-fine paradigm. There are two parts in the system where this is clearly evident. One is in the multi-level localization module, where the system tries to interchangably localize both to a general vicinity, and to a more accurate coordinate location. The second is in the process of recalling stored environment information through a form of guided (hierarchical) search using various contextual knowledge, which we believe is a key to its scalability.
In order to fairly assess our contributions, we test the system in three large scale outdoor environments - a building complex (126x180ft. area, 13966 testing images), a vegetation-filled park (270x360ft. area, 26397 testing images), and an open-field area (450x585ft. area, 34711 testing images) - each with its own challenges. We not only test its accuracy in terms of coordinate location, we also pay close attention to its efficiency in frame rate.
In the end, we describe the future directions of our research, such as how to go about inserting the localization module into a fully autonomous mobile robot system.
|Commitee:||Biederman, Irving, Sukhatme, Gaurav, Tjan, Bosco|
|School:||University of Southern California|
|School Location:||United States -- California|
|Source:||DAI-B 70/08, Dissertation Abstracts International|
|Keywords:||Biologically-inspired vision, Gist, Robot localization, Salient landmark, Vision localization|
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