Constant Space Complexity Environment Representation for Vision-based Navigation
This approach could benefit resource-constrained platforms like embedded and real-time systems by reducing computational demands.
The paper tackles the problem of complex and noisy transformations between camera image space and Euclidean space in vision-based navigation by proposing an environment representation with constant space complexity relative to camera image space, which enables planning algorithms to operate with constant run-time.
This paper presents a preliminary conceptual investigation into an environment representation that has constant space complexity with respect to the camera image space. This type of representation allows the planning algorithms of a mobile agent to bypass what are often complex and noisy transformations between camera image space and Euclidean space. The approach is to compute per-pixel potential values directly from processed camera data, which results in a discrete potential field that has constant space complexity with respect to the image plane. This can enable planning and control algorithms, whose complexity often depends on the size of the environment representation, to be defined with constant run-time. This type of approach can be particularly useful for platforms with strict resource constraints, such as embedded and real-time systems.