Enhancing Indoor Robot Pedestrian Detection Using Improved PIXOR Backbone and Gaussian Heatmap Regression in 3D LiDAR Point Clouds Article Swipe
Duy Anh Nguyễn
,
Khang Hoang Nguyen
,
Nguyen Trung Nguyen
,
Duy Anh Nguyễn
,
Hoang Ngoc Tran
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3351868
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3351868
Accurate and robust pedestrian detection is fundamental for indoor robotic systems to navigate safely and seamlessly alongside humans in spatially constrained, unpredictable indoor environments. This paper presents a novel method, IRBGHR-PIXOR, a detection framework specifically engineered for pedestrian perception in indoor mobile robots. This novel approach employs an enhanced adaptation of the cutting-edge PIXOR model, integrating two pivotal augmentations: a remodeled convolutional backbone leveraging Inverted Residual Blocks (IRB) in unison with Gaussian Heatmap Regression (GHR), as well as a Modified Focal Loss (MFL) function to tackle data imbalance issues. The IRB component notably bolsters the network’s aptitude for processing intricate spatial representations generated from sparse 3D LiDAR scans. Meanwhile, integrating GHR further elevates accuracy by enabling precise localization of pedestrian subjects. This is achieved by modeling the probability distribution and predicting the central location of individuals in the point cloud data. Extensively evaluated on the large-scale JRDB dataset comprising intense scans from 16-beam Velodyne LiDAR sensors, IRBGHR-PIXOR accomplishes exceptional results, attaining 97.17% Average Precision (AP) at the 0.5 IOU threshold. Notably, this level of accuracy is achieved without significantly increasing model complexity. By enhancing algorithms to overcome challenges in confined indoor environments, this research paves the way for safe and effective deployment of autonomous technologies once encumbered by perceptual limitations in human-centered spaces. Nonetheless, evaluating performance in diverse edge cases and integration with complementary sensory cues promise continued progress. The developments contribute towards the vital capacity of reliable dynamic perception for next-generation robotic systems coexisting in human-centric environments.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3351868
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10385189.pdf
- OA Status
- gold
- Cited By
- 8
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390781930
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390781930Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3351868Digital Object Identifier
- Title
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Enhancing Indoor Robot Pedestrian Detection Using Improved PIXOR Backbone and Gaussian Heatmap Regression in 3D LiDAR Point CloudsWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Duy Anh Nguyễn, Khang Hoang Nguyen, Nguyen Trung Nguyen, Duy Anh Nguyễn, Hoang Ngoc TranList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3351868Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10385189.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10385189.pdfDirect OA link when available
- Concepts
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Computer science, Lidar, Point cloud, Pedestrian, Pedestrian detection, Artificial intelligence, Robot, Enhanced Data Rates for GSM Evolution, Computer vision, Gaussian, Real-time computing, Remote sensing, Engineering, Geology, Quantum mechanics, Transport engineering, PhysicsTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 5, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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