Low Rank Aligning Two Point Sets for Unsupervised Correcting GPS points of Parking-slots

Parking management is an important part of urban management. Accurate Global Positioning System (GPS) points of parking-slots are the core of various applications ranging from looking for vacant parking-slots to designing the parking policies. Affected by high-rise buildings and various signals, either blocked or affected GPS points tend to mismatch the actual locations. Therefore, due to the large number of parking-slots, it is not a trivial problem to correcting raw GPS points in a unsupervised approach. In this paper, discovering that the parking-slots always most parallel with the roads in a city, we proposes an unsupervised low rank method to effectively correct GPS points of the parking-slots by rotating-translating-aligning operations. The proposed method theoretically handles any errors of GPS points; besides, the proposed unconventional alignment method is simple, yet effective and efficient for large-scale points for a city. Extensive experiments demonstrate the superiority of the proposed method.

• Publication year

  2022

• Venue

  ACM Cloud and Autonomic Computing Conference

• Publication date

  2022-11-25

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/CAC57257.2022.10054845
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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