Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation

TL;DR

This paper presents a novel edge detection method tailored for low-SNR thermal-infrared images in robotic navigation, combining denoising, Canny detection, CSS ranking, and edge linking to improve environmental mapping.

Contribution

The paper introduces a new edge detection approach specifically designed for low-quality thermal images, enhancing edge continuity and noise reduction for autonomous robot localization.

Findings

Improved edge detection accuracy in low-SNR thermal images.

Enhanced environmental mapping for robot navigation.

Comparison shows superiority over existing methods.

Abstract

In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals with utilizing thermal-infrared cameras, as opposed to conventional cameras as the primary sensor to capture images of the robot's surroundings. For localization, the images need to be further processed before feeding them to a navigational system. The main motivation of this paper was to develop an edge detection methodology capable of utilizing the low-SNR poor output from such a thermal camera and effectively detect smooth edges of the surrounding environment. The enhanced edge detector proposed in this paper takes the raw image from the thermal sensor, denoises the images, applies Canny edge detection followed by CSS method. The edges are ranked to remove any noise and only edges of the highest rank are kept. Then, the broken edges are linked by computing edge metrics and a smooth edge of the surrounding is displayed in a binary image. Several comparisons are also made in the paper between the proposed technique and the existing techniques.