A sensory mapping method, called Staggered Hierarchical Mapping (SHM), and its developmental algorithm are described in this paper. SHM is a model motivated by human early visual pathways including processing performed by the retina, Lateral Geniculate Nucleus (LGN) and the primary visual cortex. The work reported here concerns not only the design of such a series of processors but also their autonomous development. The primary goal is to address a long standing open problem of visual information processing in that processing elements that are dedicated to receptive fields of different retinal positions and different scales (sizes) must be concurrently functioning, in robotic and other applications in unstructured environments. A new Incremental Principal Component Analysis (IPCA) method is used to automatically develop orientation sensitive and other needed filters. For a fast convergence, the lateral inhibition of sensory neurons is modelled by what is called residual images. A set of staggered receptive fields models the pattern of positioning of processing cells. From sequentially sensed video frames, the proposed developing algorithm develops a hierarchy of filters, whose outputs are uncorrelated within each layer, but with increasing scale of receptive fields from low to higher layers. To study the completeness of the representation generated by the SHM, we experimentally show that the response produced at any layer is sufficient to corresponding retinal image. As an application domain, we describe out preliminary experiments of autonomous navigation by the SAIL robot, and why a mapping like the SHM is needed in our next phase of work of vision guided autonomous navigation in outdoor environments.