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A Systematic Literature Review on Long-Term Localization and Mapping for Mobile Robots
  • Ricardo B. Sousa,
  • Héber M. Sobreira,
  • Antonio Moreira
Ricardo B. Sousa
Universidade do Porto Faculdade de Engenharia

Corresponding Author:[email protected]

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Héber M. Sobreira
Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciencia
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Antonio Moreira
Universidade do Porto Faculdade de Engenharia
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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.
02 Nov 2022Submitted to Journal of Field Robotics
02 Nov 2022Submission Checks Completed
02 Nov 2022Assigned to Editor
16 Nov 2022Review(s) Completed, Editorial Evaluation Pending
17 Nov 2022Reviewer(s) Assigned
14 Jan 2023Editorial Decision: Revise Major
13 Feb 20231st Revision Received
13 Feb 2023Review(s) Completed, Editorial Evaluation Pending
13 Feb 2023Submission Checks Completed
13 Feb 2023Assigned to Editor
13 Feb 2023Reviewer(s) Assigned
26 Feb 2023Editorial Decision: Revise Minor
28 Feb 20232nd Revision Received
28 Feb 2023Submission Checks Completed
28 Feb 2023Assigned to Editor
28 Feb 2023Review(s) Completed, Editorial Evaluation Pending
28 Feb 2023Reviewer(s) Assigned
13 Mar 2023Editorial Decision: Accept