Visual Based Navigation of Mobile Robots

TL;DR

This paper presents a monocular vision-based navigation system for mobile robots that integrates obstacle detection, segmentation, and fast mapping techniques to enable indoor navigation.

Contribution

It introduces a novel algorithm combining monocular vision, SLIC segmentation, and inverse perspective mapping for efficient indoor robot navigation.

Findings

Achieved real-time obstacle detection and avoidance.

Developed a robust indoor mapping technique.

Enabled fast map updates for navigation.

Abstract

We have developed an algorithm to generate a complete map of the traversable region for a personal assistant robot using monocular vision only. Using multiple taken by a simple webcam, obstacle detection and avoidance algorithms have been developed. Simple Linear Iterative Clustering (SLIC) has been used for segmentation to reduce the memory and computation cost. A simple mapping technique using inverse perspective mapping and occupancy grids, which is robust, and supports very fast updates has been used to create the map for indoor navigation.