A quantitative study of mapping and localization algorithms on ROS based differential robot

Madhira, Kartik; Patel, Jignesh; Kothari, D. K.; Panchal, Dipesh; Patel, Dhruv

doi:10.1109/nuicone.2017.8325609

Cited by 6 publications

(1 citation statement)

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“…Popular methods include GMapping ( Grisetti et al, 2007 ; Grisetti et al, 2005 ), HectorSLAM ( Kohlbrecher and Meyer, 2012 ), and KartoSLAM ( Gerkey, 2014 ), as they are widely used in ROS ( Madhira et al, 2017 ). SLAM was originally developed for single-robot systems and its adaptation to multi-robot systems is a more recent research direction.…”

Section: Literature Reviewmentioning

confidence: 99%

Swarm SLAM: Challenges and Perspectives

2021

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A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems—i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints.

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Section: Literature Reviewmentioning

confidence: 99%