Constant Space Complexity Environment Representation for Vision-based Navigation

TL;DR

This paper proposes a novel environment representation for vision-based navigation that maintains constant space complexity, enabling efficient planning on resource-constrained platforms by directly computing per-pixel potential values from camera data.

Contribution

It introduces a constant space complexity environment representation that bypasses complex transformations, facilitating real-time planning in resource-limited systems.

Findings

Potential field computation from camera data is feasible.

Constant space complexity enables efficient real-time planning.

Applicable to resource-constrained platforms.

Abstract

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.