Outdoor mobile mapping and AI-based 3D object detection with low-cost RGB-D cameras. The use case of on-street parking statistics
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Author (Corporation)
Publication date
2021
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Collections
Type
01A - Journal article
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Parent work
Remote sensing
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Series
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Volume
13
Issue / Number
16
Pages / Duration
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Publisher / Publishing institution
MDPI
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Abstract
A successful application of low-cost 3D cameras in combination with artificial intelligence (AI)-based 3D object detection algorithms to outdoor mobile mapping would offer great potential for numerous mapping, asset inventory, and change detection tasks in the context of smart cities. This paper presents a mobile mapping system mounted on an electric tricycle and a procedure for creating on-street parking statistics, which allow government agencies and policy makers to verify and adjust parking policies in different city districts. Our method combines georeferenced red-green-blue-depth (RGB-D) imagery from two low-cost 3D cameras with state-of-the-art 3D object detection algorithms for extracting and mapping parked vehicles. Our investigations demonstrate the suitability of the latest generation of low-cost 3D cameras for real-world outdoor applications with respect to supported ranges, depth measurement accuracy, and robustness under varying lighting conditions. In an evaluation of suitable algorithms for detecting vehicles in the noisy and often incomplete 3D point clouds from RGB-D cameras, the 3D object detection network PointRCNN, which extends region-based convolutional neural networks (R-CNNs) to 3D point clouds, clearly outperformed all other candidates. The results of a mapping mission with 313 parking spaces show that our method is capable of reliably detecting parked cars with a precision of 100% and a recall of 97%. It can be applied to unslotted and slotted parking and different parking types including parallel, perpendicular, and angle parking.
Keywords
Parking statistics, Vehicel detection, Mobile mapping, Robot operating system, 3D camera, RGB-D, Performance evaluation, Convolutional neural networks, Smart city
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ISBN
ISSN
2072-4292
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
Nebiker, S., Meyer, J., Blaser, S., Ammann, M., & Rhyner, S. E. (2021). Outdoor mobile mapping and AI-based 3D object detection with low-cost RGB-D cameras. The use case of on-street parking statistics. Remote Sensing, 13(16). https://doi.org/10.3390/rs13163099