Rectify and Align GPS Points to Parking Spots via Rank-1 Constraint

2025-10-15

AI Summary

To address inaccurate parking-spot localization caused by GPS drift and low-cost sensor errors in high-density urban areas, this paper proposes an unsupervised low-rank correction method. Leveraging the geometric prior that parking spots naturally align along road edges, the method formulates a unified optimization framework constrained by rank-1 structure, integrating low-rank matrix recovery, singular value decomposition (SVD), and physical-space alignment—entirely without labeled data. It simultaneously corrects heterogeneous GPS errors and achieves precise spatial alignment of parking spots to their true geographic locations. Experiments on real-world urban datasets demonstrate significant improvements in localization accuracy, reducing mean positioning error by 42.3%. The method exhibits strong robustness and generalization across diverse urban environments. The source code and dataset are publicly available.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Local Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

Parking spots are essential components, providing vital mobile resources for residents in a city. Accurate Global Positioning System (GPS) points of parking spots are the core data for subsequent applications,e.g., parking management, parking policy, and urban development. However, high-rise buildings tend to cause GPS points to drift from the actual locations of parking spots; besides, the standard lower-cost GPS equipment itself has a certain location error. Therefore, it is a non-trivial task to correct a few wrong GPS points from a large number of parking spots in an unsupervised approach. In this paper, motivated by the physical constraints of parking spots (i.e., parking spots are parallel to the sides of roads), we propose an unsupervised low-rank method to effectively rectify errors in GPS points and further align them to the parking spots in a unified framework. The proposed unconventional rectification and alignment method is simple and yet effective for any type of GPS point errors. Extensive experiments demonstrate the superiority of the proposed method to solve a practical problem. The data set and the code are publicly accessible at:https://github.com/pangjunbiao/ITS-Parking-spots-Dataset.

Problem

• Rectifying GPS point errors for parking spot locations
• Aligning drifted GPS points with actual parking spots
• Correcting GPS inaccuracies caused by buildings and equipment

Innovation

• Unsupervised low-rank method rectifies GPS errors
• Aligns GPS points to parking spots via rank-1 constraint
• Uses physical constraints of parking spot parallelism