Monocular vision based agents for navigation in stochastic environments

Abstract

Autonomous navigation in a stochastic environment using monocular vision algorithms is a challenging task. This requires generation of depth information related to various obstacles in a changing environment. Since these algorithms depend on specific environment constraints, it is required to employee several such algorithms and select the best algorithm according to the present environment. As such modeling of monocular vision based algorithms for navigation in stochastic environments into low-end smart computing devices turns out to be a research challenge. This paper discusses a novel approach to integrate several monocular vision algorithms and to select the best algorithm among them according to the current environment conditions based on environment sensitive Software Agents. The system is implemented on an Android based mobile phone and given a sample scenario, it was able to gain a 66.6% improvement of detecting obstacles than using a single monocular vision algorithm. The CPU load was reduced by 10% when the depth perception algorithms were implemented as environment sensitive agents, in contrast to running them as separate algorithms in different threads.