Perception by autonomous systems, in unstructured dynamic worlds, is one of the significant research challenges in the development of effective intelligent systems. Nonlinear dimensionality reduction techniques have been extensively utilized within the artificial intelligence community to devise compact representations of high dimensional data. These techniques display great promise in yielding low dimensional, meaningful representations of an unstructured environment in real time from raw sensory information. Two such techniques, the kernel principal component analysis method and locally linear embedding (LLE) are evaluated herein, with respect to their ability to generate compact and physically reasonable embeddings of an unstructured environment. The LLE technique shows great potential in the computation of low dimensional and perceptually meaningful embeddings of natural environments.