A review on cloud robotics based frameworks to solve simultaneous localization and mapping (slam) problem

TL;DR

This paper reviews various cloud robotics frameworks designed to address the computationally intensive SLAM problem, highlighting their implementations and contributions to robot navigation and mapping in unknown environments.

Contribution

It provides a comprehensive review of existing cloud-based frameworks for SLAM, comparing their approaches and functionalities.

Findings

Several frameworks like DAvinCi, Rapyuta, and C2TAM effectively address SLAM challenges.

Cloud robotics enhances real-time processing and scalability for SLAM tasks.

The review identifies strengths and limitations of current frameworks.

Abstract

Cloud Robotics is one of the emerging area of robotics. It has created a lot of attention due to its direct practical implications on Robotics. In Cloud Robotics, the concept of cloud computing is used to offload computational extensive jobs of the robots to the cloud. Apart from this, additional functionalities can also be offered on run to the robots on demand. Simultaneous Localization and Mapping (SLAM) is one of the computational intensive algorithm in robotics used by robots for navigation and map building in an unknown environment. Several Cloud based frameworks are proposed specifically to address the problem of SLAM, DAvinCi, Rapyuta and C2TAM are some of those framework. In this paper, we presented a detailed review of all these framework implementation for SLAM problem.