TY - GEN
T1 - Comparison of Indoor Positioning Methods Based on AR Visual and WiFi Fingerprinting Method
AU - He, Yijun
AU - Li, Xiang
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Location-based service (LBS) has become an indispensable part of our daily life. However, indoor positioning system at early stage is not able to meet the urgent need for indoor LBS. Low-cost indoor positioning technology without additional equipment is the current challenge in LBS field. In this paper, two typical indoor positioning methods are selected: AR (Augmented Reality) based visual positioning method and WiFi based positioning method. Experiments are conducted to compare the two indoor positioning methods from multiple perspectives. Results show that performance of the two methods are similar in the aspects such as positioning time consumption, equipment cost, usability and difficulty level during preprocessing. Main differences between them are as follows: AR visual positioning method is more accurate and stable, with its mean average error at around 0.85 m and max error at 3.18 m. It’s suitable for indoor environment rich in texture and stable in light. WiFi positioning has high values in error related variables. Its MAE is about 3 m and more volatile with extreme values. However, it has an edge in usability including power consumption indicator. It’s more efficient in data acquisition stage and is suitable for large-scale positioning. This paper tends to provide reference for selection of indoor positioning methods.
AB - Location-based service (LBS) has become an indispensable part of our daily life. However, indoor positioning system at early stage is not able to meet the urgent need for indoor LBS. Low-cost indoor positioning technology without additional equipment is the current challenge in LBS field. In this paper, two typical indoor positioning methods are selected: AR (Augmented Reality) based visual positioning method and WiFi based positioning method. Experiments are conducted to compare the two indoor positioning methods from multiple perspectives. Results show that performance of the two methods are similar in the aspects such as positioning time consumption, equipment cost, usability and difficulty level during preprocessing. Main differences between them are as follows: AR visual positioning method is more accurate and stable, with its mean average error at around 0.85 m and max error at 3.18 m. It’s suitable for indoor environment rich in texture and stable in light. WiFi positioning has high values in error related variables. Its MAE is about 3 m and more volatile with extreme values. However, it has an edge in usability including power consumption indicator. It’s more efficient in data acquisition stage and is suitable for large-scale positioning. This paper tends to provide reference for selection of indoor positioning methods.
KW - AR visual positioning
KW - Comparison of indoor positioning methods
KW - Indoor positioning and navigation
KW - WiFi fingerprint indoor positioning
UR - https://www.scopus.com/pages/publications/85131142880
U2 - 10.1007/978-3-031-06245-2_13
DO - 10.1007/978-3-031-06245-2_13
M3 - 会议稿件
AN - SCOPUS:85131142880
SN - 9783031062445
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 141
EP - 151
BT - Web and Wireless Geographical Information Systems - 19th International Symposium, W2GIS 2022, Proceedings
A2 - Karimipour, Farid
A2 - Storandt, Sabine
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2022
Y2 - 28 April 2022 through 29 April 2022
ER -