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Semi-Direct SLAM with Manhattan for Indoor Low-Texture Environment

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14427))

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Abstract

Simultaneous Localization and Mapping (SLAM) with the incorporation of the Manhattan World (MW) assumption has been significantly discovered in recent years. While previous methods relied on the MW assumption to estimate camera rotation accurately, they faced limitations due to the requirement of a suitable planar environment. These constraints restricted the applicability of such systems. To overcome these limitations, we propose a novel approach that addresses the strict requirements of MW-based systems and significantly enhances tracking robustness in low-texture scenes. Our system leverages planar information in the environment to identify the presence of an MW scene. By decoupling the process, we achieve drift-free rotation estimation when the system detects an MW scene. Simultaneously, utilizing a semi-direct approach that combines point and line features to estimate translations in MW scenes, while performing full-camera pose estimation in non-MW scenes. Furthermore, we introduce a more precise loop closure detection strategy by exploiting the relative relationship between the Manhattan axes (MA) and line features in the scene. This strategy enhances the accuracy of identifying loop closures, which are crucial for SLAM systems. To evaluate the performance of our approach, we conducted experiments using public benchmarks. The results demonstrate improved pose estimation and loop closure performance compared to state-of-the-art methods. Overall, our proposed method alleviates the strict requirements of previous MW-based systems, enhances tracking robustness in low-texture scenes, and achieves improved performance in terms of pose estimation and loop closure detection.

This work was supported in part by research grants from the Science and Technology Cooperation and Exchange Special Project of Shanxi Province (No.202204041101016), the 1331 Engineering Project of Shanxi Province, and the Key Research and Development Project of Shanxi Province (NO.202102020101008).

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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Shanxi, 030006, China

    Zhiwen Zheng, He Wang & Ru Li

  2. University of Bath, Bath, BA2 7AY, UK

    Qi Zhang

Authors
  1. Zhiwen Zheng
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  2. Qi Zhang
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  3. He Wang
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  4. Ru Li
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Corresponding authors

Correspondence to Qi Zhang or Ru Li .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zheng, Z., Zhang, Q., Wang, H., Li, R. (2024). Semi-Direct SLAM with Manhattan for Indoor Low-Texture Environment. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14427. Springer, Singapore. https://doi.org/10.1007/978-981-99-8435-0_28

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  • DOI: https://doi.org/10.1007/978-981-99-8435-0_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8434-3

  • Online ISBN: 978-981-99-8435-0

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