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LiDAR-Based Obstacle Detection and Distance Estimation in Navigation Assistance for Visually Impaired

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Universal Access in Human-Computer Interaction. User and Context Diversity (HCII 2022)

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Abstract

People with visual impairments can face challenges with independent navigation and therefore may use traditional aids such as guide dogs, white canes, or a travel companion for navigation assistance. In recent years, researchers have been working on AI-based navigation assistance systems. Obstacle detection and distance estimation are two of the key challenges in such systems. In this paper, we describe a LiDAR-based obstacle detection and distance estimation technique. A lightweight deep learning-based model called EfficientDet-LiteV4 is used for obstacle detection, and a depth map from the LiDAR is used to estimate the distance to the obstacles. We have implemented and tested the approach with the LiDAR integrated into a Raspberry Pi4 board. The results show good accuracy in detecting the obstacles and in estimating distance.

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Notes

  1. 1.

    www.leddartech.com/why-lidar/.

  2. 2.

    www.intelrealsense.com/lidar-camera-l515/.

  3. 3.

    www.intelrealsense.com/sdk-2/.

  4. 4.

    www.github.com/google/automl/tree/master/efficientdet.

  5. 5.

    www.github.com/tzutalin/labelImg.

  6. 6.

    www.tensorflow.org/lite/guide/model_maker.

  7. 7.

    www.tensorflow.org/lite/performance/post_training_quantization.

  8. 8.

    www.sevensense.ai/blog/localization.

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Authors and Affiliations

  1. Department of Computer Science, Oslo Metropolitan University, Oslo, Norway

    Bineeth Kuriakose, Raju Shrestha & Frode Eika Sandnes

Authors
  1. Bineeth Kuriakose
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  2. Raju Shrestha
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  3. Frode Eika Sandnes
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Corresponding author

Correspondence to Bineeth Kuriakose .

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Editors and Affiliations

  1. Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  2. University of Crete and Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

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Kuriakose, B., Shrestha, R., Sandnes, F.E. (2022). LiDAR-Based Obstacle Detection and Distance Estimation in Navigation Assistance for Visually Impaired. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. User and Context Diversity. HCII 2022. Lecture Notes in Computer Science, vol 13309. Springer, Cham. https://doi.org/10.1007/978-3-031-05039-8_35

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  • DOI: https://doi.org/10.1007/978-3-031-05039-8_35

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  • Online ISBN: 978-3-031-05039-8

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