Enhancing Positioning Accuracy in Urban Terrain by Fusing Data from a GPS Receiver, Inertial Sensors, Stereo-Camera and Digital Maps for Pedestrian Navigation (original) (raw)
Related papers
2012
The paper presents an algorithm for estimating a pedestrian location in an urban environment. The algorithm is based on the particle filter and uses different data sources: a GPS receiver, inertial sensors, probability maps and a stereo camera. Inertial sensors are used to estimate a relative displacement of a pedestrian. A gyroscope estimates a change in the heading direction. An accelerometer is used to count a pedestrian's steps and their lengths. The so-called probability maps help to limit GPS inaccuracy by imposing constraints on pedestrian kinematics, e.g., it is assumed that a pedestrian cannot cross buildings, fences etc. This limits position inaccuracy to ca. 10 m. Incorporation of depth estimates derived from a stereo camera that are compared to the 3D model of an environment has enabled further reduction of positioning errors. As a result, for 90% of the time, the algorithm is able to estimate a pedestrian location with an error smaller than 2 m, compared to an error of 6.5 m for a navigation based solely on GPS.
Metrology and Measurement Systems, 2011
An electronic system and an algorithm for estimating pedestrian geographic location in urban terrain is reported in the paper. Different sources of kinematic and positioning data are acquired (i.e.: accelerometer, gyroscope, GPS receiver, raster maps of terrain) and jointly processed by a Monte-Carlo simulation algorithm based on the particle filtering scheme. These data are processed and fused to estimate the most probable geographical location of the user. A prototype system was designed, built and tested with a view to aiding blind pedestrians. It was shown in the conducted field trials that the method yields superior results to sole GPS readouts. Moreover, the estimated location of the user can be effectively sustained when GPS fixes are not available (e.g. tunnels).
A Particle Filter for Smartphone-Based Indoor Pedestrian Navigation
Micromachines, 2014
This paper considers the problem of indoor navigation by means of low-cost mobile devices. The required accuracy, the low reliability of low-cost sensor measurements and the typical unavailability of the GPS signal make indoor navigation a challenging problem. In this paper, a particle filtering approach is presented in order to obtain good navigation performance in an indoor environment: the proposed method is based on the integration of information provided by the inertial navigation system measurements, the radio signal strength of a standard wireless network and of the geometrical information of the building. In order to make the system as simple as possible from the user's point of view, sensors are assumed to be uncalibrated at the beginning of the navigation, and an auto-calibration procedure of the magnetic sensor is performed to improve the system performance: the proposed calibration procedure is performed during regular user's motion (no specific work is required). The navigation accuracy achievable with the proposed method and the results of the auto-calibration procedure are evaluated by means of a set of tests carried out in a university building. Since the use of none of the considered approaches (INS and RSS) can allow by itself obtaining a sufficiently good estimation error in the considered conditions of interest, a commonly adopted strategy to tackle this problem relies on the integration between the data collected by several types of sensors to achieve more robust localization results . In particular, in this paper, an indoor navigation system with minimal positioning sensor equipment is considered: the goal of this work is to enable navigation with low-cost mobile devices (typically carried by the user's hand) in indoor and other critical environments, e.g., the proposed navigation algorithm can be executed on a smartphone, which estimates its own position inside a building by combining the information collected from the Wi-Fi network (RSS) with measurements derived from the embedded INS sensor (the smartphone considered here is provided with a three-axis accelerometer and three-axis magnetometer). Furthermore, a map of the building is assumed to be available to the tracking algorithm: this information can be either provided by the Wi-Fi network or acquired by image plots or plans of the building.
Data Integration from GPS and Inertial Navigation Systems for Pedestrians in Urban Area
TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 2013
The GPS system is widely used in navigation and the GPS receiver can offer long-term stable absolute positioning information. The overall system performance depends largely on the signal environments. The position obtained from GPS is often degraded due to obstruction and multipath effect caused by buildings, city infrastructure and vegetation, whereas, the current performance achieved by inertial navigation systems (INS) is still relatively poor due to the large inertial sensor errors. The complementary features of GPS and INS are the main reasons why integrated GPS/INS systems are becoming increasingly popular. GPS/INS systems offer a high data rate, high accuracy position and orientation that can work in all environments, particularly those where satellite availability is restricted. In the paper integration algorithm of GPS and INS systems data for pedestrians in urban area is presented. For data integration an Extended Kalman Filter (EKF) algorithm is proposed. Complementary characteristics of GPS and INS with EKF can overcome the problem of huge INS drifts, GPS outages, dense multipath effect and other individual problems associated with these sensors. http://www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 7 Number 3
Application of particle filters for indoor positioning using floor plans
2010
This paper presents a numerical approach to the pedestrian map-matching problem using building plans. The proposed solution is based on a sequential Monte Carlo method, so called particle filtering. This algorithm can be adapted for implementation on real-time pedestrian navigation systems using low-cost MEMS gyroscopes and accelerometers as deadreckoning sensors. The algorithm reliability and accuracy performance was investigated using simulated data typical for pedestrians walking inside building. The results show that this map-aided dead reckoning system is able to provide accurate indoor positioning for long periods of time without using GPS data.
Bayesian approach for indoor pedestrian localisation
2006
The principal concept of navigation is to start from a known (initial) position and to ensure a continued and reliable localisation of the user during his/her movement. The initial position of the trajectory is usually obtained via GPS or defined by the user. Consider a pedestrian navigation system which contains a GPS receiver and a set of inertial sensors, connected with a map database. In the urban environment and indoors the localisation depends entirely on the measurements from the inertial sensors. The trajectory is defined in a local coordinate system and with an arbitrary orientation. The problem to solve is to determine the user's location using the map database and inertial measurements of the navigation system. The idea behind our approach is to find the location and orientation of the trajectory and thus the user's location. The proposed solution associates the user's trajectory with the map database applying statistical methods in combination with map-matching. Similar geometric forms must be identified in both the trajectory and the link-node model. The trajectory, defined by a set of consecutive points, is transformed to a set of lines thanks to a dedicated motion model. In this research we propose a solution based on statistical methods where the history of the route and actual measurements are treated at the same time. The determination of the absolute position is entirely represented by its probability density function (PDF) in the frame of Bayesian inference. Following this approach the posterior estimation of the user's location can be calculated using prior information and actual measurements. Because of the non-linear nature of the estimation problem, non-linear filtering techniques like particle filters (Sequential Monte Carlo methods) are applied.
International Journal of Security and Its Applications, 2015
When Global Positioning Systems are obstructed, standalone pedestrian tracking can be very daunting. Users in such obstructed environments (especially in home environments) will find it difficult to perform on-site navigation. It is important to create a standalone pedestrian tracking system that provides better location determination services with less computational complexity and deployment cost. One promising way to implement this service is through the use of Inertial Measurement Unit (IMU) sensors. This tracking method provides the pinpointing of standalone tracking information but is handicapped by missing stance phase during pedestrian walking activities. A new pedestrian stance detection using simultaneously localization and mapping (SLAM) will be designed in this paper with a focus on robust indoor positioning systems. We will present our preliminary results to illustrate the performance of the system for an indoor environment setup at the end of this paper.
Two different approaches for augmented GPS pedestrian navigation
International Symposium on …, 2001
This paper present the calibration of the different models used for pedestrian navigation. Information on travelled distance and azimuth sensed by inertial sensors is merged with GPS observation through Kalman filtering. All models use GPS positions without differential corrections to calibrate systematic errors present in inertial sensors. Different prototypes have been developed. They integrate a digital magnetic compass or gyroscopes, tri-or bi-axial accelerometers, an altimeter and a mono-frequency GPS receiver.
International Journal of Electrical and Computer Engineering (IJECE), 2023
This article presents a new method for land vehicle navigation using global positioning system (GPS), dead reckoning sensor (DR), and digital road map information, particularly in urban environments where GPS failures can occur. The odometer sensors and map measure can be used to provide continuous navigation and correct the vehicle location in the presence of GPS masking. To solve this estimation problem for vehicle navigation, we propose to use particle filtering for GPS/odometer/map integration. The particle filter is a method based on the Bayesian estimation technique and the Monte Carlo method, which deals with non-linear models and is not limited to Gaussian statistics. When the GPS sensor cannot provide a location due to the number of satellites in view, the filter fuses the limited GPS pseudo-range data to enhance the vehicle positioning. The developed filter is then tested in a transportation network scenario in the presence of GPS failures, which shows the advantages of the proposed approach for vehicle location compared to the extended Kalman filter. This is an open access article under the CC BY-SA license.
Localization system for pedestrians based on sensor and information fusion
17Th International Conference on Information Fusion Fusion 2014, 2014
Nowadays there is an increase of location-aware mobile applications. However, these applications only retrieve location with a mobile device's GPS chip. This means that in indoor or in more dense environments these applications don't work properly. To provide location information everywhere a pedestrian Inertial Navigation System (INS) is typically used, but these systems can have a large estimation error since, in order to turn the system wearable, they use low-cost and low-power sensors. In this work a pedestrian INS is proposed, where force sensors were included to combine with the accelerometer data in order to have a better detection of the stance phase of the human gait cycle, which leads to improvements in location estimation. Besides sensor fusion an information fusion architecture is proposed, based on the information from GPS and several inertial units placed on the pedestrian body, that will be used to learn the pedestrian gait behavior to correct, in real-time, the inertial sensors errors, thus improving location estimation.