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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.
Highlights
Government agencies are interested in freeing street space—for example, that which is currently occupied by on-street parking—to accommodate new lanes for bikes etc. to support and promote more sustainable traffic modes
Capabilities and performance of the overall system: themain lowOur proposed low-cost and object detection approach consists of three cost navigation unit, the the low-cost
We addressed the challenge of reliably detecting and localizing vehicles in the point clouds derived from the low-cost 3D cameras, which are significantly noisier and have more data gaps than LiDAR-based point clouds
Summary
We are currently witnessing a transformation of urban mobility from motorized individual transport towards an increasing variety of multimodal mobility offerings, including public transport, dedicated bike paths, and various ridesharing services for cars, bikes, e-scooters, and the like. These offerings, on the one hand, are expected to decrease the need for on-street parking spaces and the undesirable traffic associated with searching for available parking spots, which has been shown to account for an average of 30% of the total traffic in major cities [1]. Government agencies are interested in freeing street space—for example, that which is currently occupied by on-street parking—to accommodate new lanes for bikes etc. to support and promote more sustainable traffic modes
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