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Related Experiment Video

Updated: Aug 30, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization.

Chao Li¹, Wennan Chai², Xiaohui Yang³

¹College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

This summary is machine-generated.

This study introduces a crowdsourcing method for indoor mapping and localization using graph optimization. The approach enhances accuracy and reliability compared to traditional single-track semantic maps.

Keywords:

crowdsourcing graph optimization localization mapping multi-sensor fusion object detector

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Published on: May 7, 2019

Area of Science:

Computer Science
Robotics
Geographic Information Systems

Background:

Smartphones with multiple sensors enable crowdsourcing for indoor mapping.
Existing methods often rely on single-track semantic maps, which can be limited in accuracy.

Purpose of the Study:

To propose a crowdsourcing-based method for indoor semantic map construction and localization.
To improve the quality and accuracy of indoor maps and localization using graph optimization.

Main Methods:

Constructing an optimization graph using waypoints, semantic, and Wi-Fi landmarks as nodes.
Initializing the venue map with the highest quality single-track semantic map.
Optimizing aligned venue and candidate maps, then updating the venue map attributes.

Main Results:

Crowdsourcing-based venue maps demonstrated superiority over single-track semantic maps in experiments.
The landmark matching-based localization method achieved a mean error of less than 0.5 m.

Conclusions:

The proposed crowdsourcing method effectively constructs accurate indoor semantic maps.
This approach significantly improves indoor localization accuracy compared to existing methods.