Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Abstract

Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error.

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

“…In parallel, the secondary PF samples from the main PF when outliers in the RSS measurements are detected. Lastly, an infrastructure-independent hybrid localisation system was proposed in [ 49 ], which exploited the smartphone’s IMU and camera for fusion of PDR and camera-based Simultaneous Mapping and Localisation (SLAM).…”

Yeni Bir İç Mekân Konum Bulma Sistemi

“…Konum alıcı-verici elektronik kartın X ve Y koordinatları, Cramer Rao kuralını kullanarak denklem (2) çözülerek bulunur [6]. V. KAYNAKLAR…”

“…Yapılan çalışmalarda doğru sonuçlara ulaşabilmek için kızılötesi alıcı-verici sistemler, iBeacon teknolojisi, cep telefonu, RFID (Radio Frequency Identification; Radyo Frekansı ile Tanımlama) teknolojisi…gibi farklı materyal kullanılmıştır [3,5,[16][17][18][19][20][21][22]. Kullanılan materyallerin başında akıllı telefonlar gelmektedir [4][5][6][7]. Akıllı telefonlarla doğru sonuçlara yaklaşma oranı artarken maliyette doğru orantılı şekilde artmaktadır.…”

Vision-Based Methodology for Characterizing the Flow of a High-Density Crowd on Footbridges: Strategy and Application

“…The cumulative errors encountered by dead reckoning can be reduced by sensor fusing techniques. Force instance, in [34], a hybrid approach for the indoor localization is proposed which combines the data from the inertial measurement unit with the smartphone camera for the optimal estimation of the location. In [35], the trajectory of a micro aerial vehicle is established combining the inertial measurement unit with a camera that registers markers.…”