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Efficient Deep Learning-based Semantic Mapping Approach using Monocular Vision for Resource-Limited Mobile Robots

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Abstract

In recent years, the demand for robots is not only limited to sophisticated industrial setups, there exists an unprecedented demand for low-cost robots in living places with the capabilities of performing human-centric operations. For the semantic-rich mapping of random environments, current state-of-the-art techniques include sophisticated hardware like Kinect sensor, Lidar, deep learning (DL)-based vision, and stereo vision-based systems. Inevitably, these systems increase the cost of the product which requires expensive hardware for processing the information. It, therefore, creates a hurdle to implementing them on low-cost service robots where interaction matters more than precision. To overcome these issues, in this paper, we propose two novel techniques: 1) a light, yet efficient, semantic mapping technique for scene-wise localization of objects by combining object detection and camera geometry; 2) an accurate and robust novel integration technique for coalition of scene-wise information for large-scale maps. The main goal of this framework is to host a semantic mapping process on a limited processing device like Raspberry Pi. The semantic information can be further integrated into any Human-Robot Interaction (HRI) system. A tensorflow-lite version of Single Shot Detection (SSD) for object detection, a wheel odometer for odometry tracking, and pinhole camera geometry are used for the whole mapping process. The proposed model has demonstrated promising results by accurately mapping the environment with semantic-rich features. Current work is time efficient and suitable for object-orientated task execution of low-cost robots, such as smart toys and other smart home gadgets.

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Data Availability

The data that support the findings of this study are available to reasonable request.

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Acknowledgements

The support of the Aeronautical Research and Development Board (Grant No. DARO/08/1051450/M/I) is gratefully acknowledged.

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Authors and Affiliations

  1. Center of Intelligent Robots, Indian Institute of Information Technology, Allahabad, India

    Aditya Singh

  2. SFI for Research Training in Artificial Intelligence, Dublin City University, Dublin 9, Ireland

    Kislay Raj

  3. Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA

    Arunabha M. Roy

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  1. Aditya Singh
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  2. Kislay Raj
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  3. Arunabha M. Roy
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Contributions

AS, KR, AMR - Conceptualization; AS, KR - Data curation; Formal analysis; Investigation; Methodology; Software; Validation; Visualization; Roles/Writing - original draft; Writing - review, and editing. AA, KR Formal analysis; Investigation; Methodology; Roles/Writing - original draft; AS, KR, AMR - Writing, review, and editing; AMR- Supervision; Project Administration.

Corresponding author

Correspondence to Arunabha M. Roy.

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Singh, A., Raj, K. & Roy, A.M. Efficient Deep Learning-based Semantic Mapping Approach using Monocular Vision for Resource-Limited Mobile Robots. J Intell Robot Syst 109, 69 (2023). https://doi.org/10.1007/s10846-023-01988-y

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