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.
Highlights
Indoor localization systems are classified as either building dependent or building independent based on the sensors used for localization [1]
The inertial measurement unit (IMU) sensor used in pedestrian dead reckoning (PDR) systems includes the accelerometer, magnetometer and gyroscope sensors and these sensors give user position based on user heading and step length information
We developed a simultaneous localization and mapping (SLAM) by fusion of keypoints and squared planar markers (UcoSLAM) algorithm proposed by Munoz-Salinas et al [11] for the camera based localization system by adding markers to the experiment area
Summary
Indoor localization systems are classified as either building dependent or building independent based on the sensors used for localization [1]. The limited level of accuracy in IMU sensors is due to the accumulated errors from accelerometer, drift errors from gyroscope and external magnetic fields that affect the magnetometer. These sensor errors degrade indoor position accuracy, the need for compensation. In the proposed hybrid systems, the results from the IMU-based system are used to compensate the heading error from the camera-based system. Experimental results demonstrate the effect of the proposed hybrid fusion method and the proposed hybrid method reduces the sensor errors for IMU localization and heading error for the camera based localization system.
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