Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.

An Enhanced Pedestrian Visual-Inertial SLAM System Aided with Vanishing Point in Indoor Environments

The visual-inertial simultaneous localization and mapping (SLAM) is a feasible indoor positioning system that combines the visual SLAM with inertial navigation. There are accumulated drift errors in inertial navigation due to the state propagation and the bias of the inertial measurement unit (IMU) sensor. The visual-inertial SLAM can correct the drift errors via loop detection and local pose optimization. However, if the trajectory is not a closed loop, the drift error might not be significantly reduced. This paper presents a novel pedestrian dead reckoning (PDR)-aided visual-inertial SLAM, taking advantage of the enhanced vanishing point (VP) observation. The VP is integrated into the visual-inertial SLAM as an external observation without drift error to correct the system drift error. Additionally, the estimated trajectory’s scale is affected by the IMU measurement errors in visual-inertial SLAM. Pedestrian dead reckoning (PDR) velocity is employed to constrain the double integration result of acceleration measurement from the IMU. Furthermore, to enhance the proposed system’s robustness and the positioning accuracy, the local optimization based on the sliding window and the global optimization based on the segmentation window are proposed. A series of experiments are conducted using the public ADVIO dataset and a self-collected dataset to compare the proposed system with the visual-inertial SLAM. Finally, the results demonstrate that the proposed optimization method can effectively correct the accumulated drift error in the proposed visual-inertial SLAM system.

Drift Control of Pedestrian Dead Reckoning (PDR) for Long Period Navigation under Different Smartphone Poses

The inherent errors of low-cost inertial sensors cause significant heading drift that accumulates over time, making it difficult to rely on Pedestrian Dead Reckoning (PDR) for navigation over a long period. Moreover, the flexible portability of the smartphone poses a challenge to PDR, especially for heading determination. In this work, we aimed to control the PDR drift under the conditions of the unconstrained smartphone to eventually enhance the PDR performance. To this end, we developed a robust step detection algorithm that efficiently captures the peak and valley events of the triggered steps regardless of the device’s pose. The correlation between these events was then leveraged as distinct features to improve smartphone pose detection. The proposed PDR system was then designed to select the step length and heading estimation approach based on a real-time walking pattern and pose discrimination algorithm. We also leveraged quasi-static magnetic field measurements that have less disturbance for estimating reliable compass heading and calibrating the gyro heading. Additionally, we also calibrated the step length and heading when a straight walking pattern is observed between two base nodes. Our results showed improved device pose recognition accuracy. Furthermore, robust and accurate results were achieved for step length, heading and position during long-term navigation under unconstrained smartphone conditions.

Technologies supporting independent moving inside buildings for people with visual impairment

Sustainable Development Goals (SDGs) include disability and persons with disabilities for example partially sighted or blind. Disability is referenced in multiple parts of the SDGs, specifically in the parts related to education, growth and employment, inequality, accessibility of human settlements and buildings. The paper presents selected technologies that support independent movement blind people inside huge buildings. The paper will refer to two SDGs: No 9 and No 11. There needs to be a future in which cities provide opportunities for all with access to basic services, housing, friendly public buildings, transportation and more, even to people with eye disabilities. This paper presents selected systems for finding objects or places, recognizing objects inside rooms and navigation inside buildings based on nonradio and wireless technologies. The following technologies and solutions were presented and compared: physical items, smartphone cameras, laser rangefinders, pedestrian dead-reckoning, intelligent lighting, Wi-Fi, BLE beacons, magnetic fields and barometric pressure sensors.

Maximum convergence algorithm for WiFi based indoor positioning system

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points.

A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service.

LLIO: Lightweight Learned Inertial Odometer

<div><div>The 3D position estimation of pedestrians is a vital module to build the connections between persons and things.</div><div>The traditional gait model-based methods cannot fulfill the various motion patterns.</div><div>And the various data-driven-based inertial odometry solutions focus on the 2D trajectory estimation on the ground plane, which is not suitable for AR applications.</div><div>TLIO (Tight Learned Inertial Odometry) proposed an inertial-based 3D motion estimator that achieves very low position drift by using the raw IMU measurements and the displacement predict coming from a neural network to provide low drift pedestrian dead reckoning.</div><div>However, TLIO is unsuitable for mobile devices because it is computationally expensive.</div><div>In this paper, a lightweight learned inertial odometry network (LLIO-Net) is designed for mobile devices.</div><div>By replacing the network in TLIO with the LLIO-Net, the proposed system shows similar accuracy but significantly improved efficiency.</div><div>Specifically, the proposed LLIO algorithm was implemented on mobile devices and compared the efficiency with TLIO.</div><div>The inference efficiency of the proposed system is 2-12 times that of TLIO.</div></div>

LLIO: Lightweight Learned Inertial Odometer

<div><div>The 3D position estimation of pedestrians is a vital module to build the connections between persons and things.</div><div>The traditional gait model-based methods cannot fulfill the various motion patterns.</div><div>And the various data-driven-based inertial odometry solutions focus on the 2D trajectory estimation on the ground plane, which is not suitable for AR applications.</div><div>TLIO (Tight Learned Inertial Odometry) proposed an inertial-based 3D motion estimator that achieves very low position drift by using the raw IMU measurements and the displacement predict coming from a neural network to provide low drift pedestrian dead reckoning.</div><div>However, TLIO is unsuitable for mobile devices because it is computationally expensive.</div><div>In this paper, a lightweight learned inertial odometry network (LLIO-Net) is designed for mobile devices.</div><div>By replacing the network in TLIO with the LLIO-Net, the proposed system shows similar accuracy but significantly improved efficiency.</div><div>Specifically, the proposed LLIO algorithm was implemented on mobile devices and compared the efficiency with TLIO.</div><div>The inference efficiency of the proposed system is 2-12 times that of TLIO.</div></div>