Name
Title | ||
|---|---|---|
Evaluation Criteria for Inside-Out Indoor Positioning Systems Based on Machine Learning |
Abstract | ||
|---|---|---|
Real-time tracking allows to trace goods and enables the optimization of logistics processes in many application areas. Camera-based inside-out tracking that uses an infrastructure of fixed and known markers is costly as the markers need to be installed and maintained in the environment. Instead, systems that use natural markers suffer from changes in the physical environment. Recently a number of approaches based on machine learning (ML) aim to address such issues. This paper proposes evaluation criteria that consider algorithmic properties of ML-based positioning schemes and introduces a dataset from an indoor warehouse scenario to evaluate for them. Our dataset consists of images labeled with millimeter precise positions that allows for a better development and performance evaluation of learning algorithms. This allows an evaluation of machine learning algorithms for monocular optical positioning in a realistic indoor position application for the first time. We also show the feasibility of ML-based positioning schemes for an industrial deployment. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IPIN.2018.8533862 | 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) |
Keywords | Field | DocType |
performance evaluation,learning algorithms,monocular optical positioning,realistic indoor position application,ML-based positioning schemes,evaluation criteria,indoor positioning systems,machine learning,real-time tracking,logistics processes,application areas,fixed markers,natural markers,physical environment,algorithmic properties,dataset,indoor warehouse scenario,millimeter precise positions,camera-based inside-out tracking | Software deployment,Artificial intelligence,Engineering,Monocular,Machine learning | Conference |
ISSN | ISBN | Citations |
2162-7347 | 978-1-5386-5636-5 | 2 |
PageRank | References | Authors |
0.37 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christoffer Löffler | 1 | 5 | 2.18 |
| Sascha Riechel | 2 | 2 | 0.37 |
| Janina Fischer | 3 | 2 | 0.37 |
| Christopher Mutschler | 4 | 62 | 11.63 |