Voxel-Based Point Cloud Localization for Smart Spaces Management

TL;DR

This paper introduces a voxel-based localization method using point cloud data for efficient management of smart urban spaces, demonstrating accurate sensor positioning for applications like smart parking.

Contribution

It presents a novel voxel-based approach combining ICP and RANSAC for precise localization in urban environments, suitable for smart space management.

Findings

Accurate sensor position estimation demonstrated

Effective point cloud registration using ICP and RANSAC

Potential for smart parking space management

Abstract

This paper proposes a voxel-based approach for creating a digital twin of an urban environment that is capable of efficiently managing smart spaces. The paper explains the registration and localization procedure of the point cloud dataset, which uses the KISS ICP for scan point cloud combination and the RANSAC method for the initial alignment of the combined point cloud. The mobile mapping point cloud using Riegl VMX-250 serves as the reference map, and Velodyne scans are used for localization purposes. The point-to-plane iterative closest-point method is then employed to refine the alignment. The paper evaluates the efficacy of the proposed method by calculating the errors between the estimated and ground truth positions. The results indicate that the voxel-based approach is capable of accurately estimating the position of the sensor platform, which are applicable for various use cases. A specific use case in the context is smart parking space management, which is described and initial visualization results are shown.