There is a large gap between the visual capabilities of biological organisms and visual capabilities of autonomous robots. Even the most simple of flying insects is able to fly within complex environments, locate food, avoid obstacles and elude predators with seeming ease. This stands in stark contrast to even the most advanced of modern ground based or flying autonomous robots, which are only capable of autonomous navigation within simple environments and will fail spectacularly if the expected environment is modified even slightly. This dissertation provides a narrative of the author's graduate research into biologically inspired algorithms for visual perception and navigation with autonomous robotics applications. This research led to several novel algorithms and neural network implementations, which provide improved capabilities of visual sensation with exceedingly light computational requirements. A new computationally-minimal approach to visual motion detection was developed and demonstrated to provide obstacle avoidance without the need for directional specificity. In addition, a novel method of calculating sparse range estimates to visual object boundaries was demonstrated for localization, navigation and mapping using one-dimensional image arrays. Lastly, an assembly of recurrent inhibitory neural networks was developed to provide multiple concurrent object detection, visual feature binding, and internal neural representation of visual objects. These algorithms are promising avenues for future research and are likely to lead to more general, robust and computationally minimal systems of passive visual sensation for a wide variety of autonomous robotics applications.