Monocular Navigation in Large Scale Dynamic Environments

TL;DR

This paper introduces a monocular method for robustly reconstructing motion in large-scale dynamic environments, effectively handling independent agent movements where traditional stereo or structure-from-motion methods struggle.

Contribution

The authors develop a novel mathematical framework for monocular motion reconstruction that separates direction and magnitude, improving robustness in dynamic, large-scale scenarios.

Findings

Effective separation of motion direction and magnitude

Robust reconstruction despite small disparity signals

Applicable to large-scale dynamic environments

Abstract

We present a processing technique for a robust reconstruction of motion properties for single points in large scale, dynamic environments. We assume that the acquisition camera is moving and that there are other independently moving agents in a large environment, like road scenarios. The separation of direction and magnitude of the reconstructed motion allows for robust reconstruction of the dynamic state of the objects in situations, where conventional binocular systems fail due to a small signal (disparity) from the images due to a constant detection error, and where structure from motion approaches fail due to unobserved motion of other agents between the camera frames. We present the mathematical framework and the sensitivity analysis for the resulting system.