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\begin{document}

\title{A Systematic Literature Review on Long-Term Localization and Mapping for
Mobile Robots}



\author[1]{Ricardo B. Sousa}%
\author[2]{Héber M. Sobreira}%
\author[1]{Antonio  Moreira}%
\affil[1]{Universidade do Porto Faculdade de Engenharia}%
\affil[2]{Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciencia}%


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  \date{May 17, 2024}


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\begin{abstract}
Long-term operation of autonomous robots creates new challenges to the
Simultaneous Localization and Mapping (SLAM). Varying conditions of the
vehicle's surroundings, such as appearance variations (lighting,
daytime, weather, or seasonal) or reconfigurations of the environment,
are a challenge for SLAM algorithms to adapt to new changes while
preserving old states. When also operating for long periods and
trajectory lengths, the map should readjust to environment changes but
not grow indefinitely, where the map size should be dependent only on
the explored environment area. Long-term SLAM intends to overcome the
challenges associated with lifelong autonomy and improve the robustness
of autonomous systems. Although several studies review SLAM algorithms,
none of them focus on lifelong autonomy. Thus, this paper presents a
systematic literature review on long-term localization and mapping
following the Preferred Reporting Items for Systematic reviews and
Meta-Analysis (PRISMA) guidelines. The review analyzes 142 works
covering appearance invariance, modeling the environment dynamics, map
size management, multi-session, and computational issues including
parallel computing and timming efficiency. The analysis also focus on
the experimental data and evaluation metrics commonly used to assess
long-term autonomy. Moreover, an overview over the bibliographic data of
the 142 records provides analysis in terms of keywords and authorship
co-occurrence to identify the terms more used in long-term SLAM and
research networks between authors, respectively. Future studies can
update this paper thanks to the systematic methodology presented in the
review and the public GitHub repository with all the documentation and
scripts used during the review process.%
\end{abstract}%



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