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Enhancing SLAM efficiency: a comparative analysis of B-spline surface mapping and grid-based approaches

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Abstract

Environmental mapping serves as a crucial element in Simultaneous Localization and Mapping (SLAM) algorithms, playing a pivotal role in ensuring the accurate representation necessary for autonomous robot navigation guided by SLAM. Current SLAM systems predominantly rely on grid-based map representations, encountering challenges such as measurement discretization for cell fitting and grid map interpolation for online posture prediction. Splines present a promising alternative, capable of mitigating these issues while maintaining computational efficiency. This paper delves into the efficiency disparities between B-Spline surface mapping and discretized cell-based approaches, such as grid mapping, within indoor environments. B-Spline Online SLAM and FastSLAM, utilizing Rao-Blackwellized Particle Filter (RBPF), are employed to achieve range-based mapping of the unknown 2D environment. The system incorporates deep learning networks in the B-Spline curve estimation process to compute parameterizations and knot vectors. The research implementation utilizes the Intel Research Lab benchmark dataset to conduct a comprehensive qualitative and quantitative analysis of both approaches. The B-Spline surface approach demonstrates significantly superior performance, evidenced by low error metrics, including an average squared translational error of 0.0016 and an average squared rotational error of 1.137. Additionally, comparative analysis with Vision Benchmark Suite demonstrates robustness across different environments, highlighting the effectiveness of B-Spline SLAM for real-world applications.

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Data Availability and Access

The dataset supporting the findings of this study is derived from the open dataset provided by the Intel Research Lab benchmark and is publicly accessible for research purposes [34, 35]. The authors affirm compliance with the dataset’s terms and conditions and emphasize that, due to anonymization and de-identification, individual informed consent is not applicable.

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Acknowledgements

The authors would like to express their gratitude to the Rajiv Gandhi National Institute of Youth Development, Sriperumbudur, India, and the Vellore Institute of Technology in Chennai, India, for giving valuable research facilities. The authors also thank Mr. Ganesh, VIT Chennai, and Mr. Sethuraman T V, VIT Chennai for sparing time and sharing their expertise towards the enhancement of this research work. Funded by Seed research grant in engineering management and sciences, VIT Chennai. Photonic disinfection to combat COVID-19 using Indoor unmanned aerial vehicle

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Authors and Affiliations

  1. Computer Science - Artificial Intelligence and Machine Learning, Rajiv Gandhi National Institute of Youth Development, Pennalur, Sriperumbudur, 602105, Tamilnadu, India

    B. Rajesh Kanna

  2. Computer Science, University of California, Davis, Shields Ave, Davis, CA, 95616, USA

    Shreyas Madhav AV

  3. School of Computer Science & Engineering, Vellore Institute of Technology, Melakottaiyur, Chennai, 600127, Tamilnadu, India

    C. Sweetlin Hemalatha

  4. School of Electronics Engineering, Vellore Institute of Technology, Melakottaiyur, Chennai, 600127, Tamilnadu, India

    Manoj Kumar Rajagopal

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Contributions

The collaborative effort of the authors in this study is outlined as follows: Dr. B. Rajesh Kanna played a crucial role in conceptualizing the study, designing and implementing the methodology, and contributed significantly to both the initial drafting and subsequent revisions of the manuscript. Shreyas Madhav AV actively participated in the development of the research design and methodology, played a key role in data analysis and interpretation, made substantial contributions to the manuscript’s writing, and took primary responsibility for data collection and analysis. Dr. C. Sweetlin Hemalatha provided valuable critical feedback during the revision process, contributed to the conceptualization and design of the study, played a substantial role in drafting and revising the manuscript, and offered insightful perspectives during the interpretation of results. Dr. Manoj Kumar Rajagopal contributed to formulating the research question and study design, actively engaged in data analysis and interpretation, and provided critical input during the revision process.

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Correspondence to Manoj Kumar Rajagopal.

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Ethical and Informed Consent for Data Used

This manuscript should not be submitted to multiple journals for simultaneous consideration. The submitted work should be original and should not have been published elsewhere in any form or language (partially or in full). The data utilized in this study were sourced from the open dataset provided by the Intel Research Lab benchmark. This dataset is publicly accessible and intended for research purposes. The authors acknowledge and comply with the terms and conditions outlined by the dataset provider. As an open dataset, individual informed consent is not applicable, given that the data is anonymized and de-identified.

Competing Interests

The author of the manuscript entitled “Enhancing SLAM Efficiency: A Comparative Analysis of B-Spline Surface Mapping and Grid-Based Approaches for Autonomous Robot Navigation” declares (s) that there is no conflict of interest with anyone.

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Kanna, B.R., AV, S.M., Hemalatha, C.S. et al. Enhancing SLAM efficiency: a comparative analysis of B-spline surface mapping and grid-based approaches. Appl Intell 54, 10802–10818 (2024). https://doi.org/10.1007/s10489-024-05776-5

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