Chris Hide

Aiding MEMS IMU with building heading for indoor pedestrian navigation

Heading drift error remains a problem in a standalone navigation system that uses only low cost MEMS IMU due to yaw error unobservability. This paper therefore proposes a shoe mounted IMU approach, integrated with ZUPT and building heading information in Kalman filter environment to reduce heading drift for pedestrian navigation application. There were no additional sensors used except MEMS IMU that contains accelerometers and gyros. Two trials; represented by regular and irregular walking trials, were undertaken in a typical public building. The results were then compared with HSGPS solution and IMU+ZUPT solution. Based on these trials, return position error of 0.1% from total distance travelled was achieved using a low cost MEMS IMU only.

Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation

In environments where GNSS is unavailable or not useful for positioning, the use of low cost MEMS-based inertial sensors has paved a way to a more cost effective solution. Of particular interest is a foot mounted pedestrian navigation system, where zero velocity updates (ZUPT) are used with the standard strapdown navigation algorithm in a Kalman filter to restrict the error growth of the low cost inertial sensors. However heading drift still remains despite using ZUPT measurements since the heading error is unobservable. External sensors such as magnetometers are normally used to mitigate this problem, but the reliability of such an approach is questionable because of the existence of magnetic disturbances that are often very difficult to predict. Hence there is a need to eliminate the heading drift problem for such a low cost system without relying on external sensors to give a possible stand-alone low cost inertial navigation system. In this paper, a novel and effective algorithm for generating heading measurements from basic knowledge of the orientation of the building in which the pedestrian is walking is proposed to overcome this problem. The effectiveness of this approach is demonstrated through three field trials using only a forward Kalman filter that can work in real-time without any external sensors. This resulted in position accuracy better than 5 m during a 40 minutes walk, about 0·1% in position error of the total distance. Due to its simplistic algorithm, this simple yet very effective solution is appealing for a promising future autonomous low cost inertial navigation system.

Low cost vision-aided IMU for pedestrian navigation

Low cost MEMS sensors typically result in large position errors after very short periods of time unless they are frequently corrected by measurements from other systems. One form of measurements comes from the computer vision community where successive frames from a camera approximately looking at the ground can be used to compute the translation between frames. These measurements can be used to control the drift of an Inertial Measurement Unit (IMU) when measurements from other systems such as GPS are not available. This configuration of sensors is preferable since they are already available on some smartphones. This paper demonstrates that computer vision measurements can significantly reduce the drift of IMU-only positioning with a view for pedestrian navigation indoors. Issues such as computational requirements and operation in low light areas are also discussed.

Using Constraints for Shoe Mounted Indoor Pedestrian Navigation

Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’. The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

A particle filter approach to indoor navigation using a foot mounted inertial navigation system and heuristic heading information

Foot mounted inertial navigation is an effective method for obtaining high quality pedestrian navigation solutions from MEMS sensors. Zero-Velocity information from stationary periods in the step-cycle can be used to regularly correct position drift and update estimates of the inertial sensor biases, hence dramatically improving the navigation solution. However the causes of heading error remain poorly observable and so foot mounted inertial navigation suffers from considerable drift over time. To address this problem the authors previously developed Cardinal Heading Aided Inertial Navigation (CHAIN). CHAIN makes use of the fact that when in a building, obstacles such as corridors and furniture constrain pedestrians to move in one of four directions parallel to the outside walls of the building. This knowledge is then appropriately weighted and used in an Extended Kalman Filter to improve error estimation. Although the CHAIN method is very effective at improving the quality of the heading estimates, position errors still accumulate with time, and threshold tests are required to cope with periods of motion away from the cardinal headings. In this work we investigate the use of a building floor plan to further aid navigation. This is achieved using a particle filter approach whereby particles which cross walls are removed and those which navigate in open spaces are allowed to continue. Previously the particle filter approach has been computationally intensive process requiring many particles to effectively model the navigation errors. In our work we recognise that heading is the primary source of navigation error and therefore incorporate heuristic heading information into the particle filter design. By weighting particles according to their heading we reduce the number of particles required to maintain a small failure rate and improve system performance in more open areas where there are few mapped walls to aid navigation. This paper will describe the design of our particle filter and the heuristic heading approach. Results from a number of representative test walks using a MEMS IMU will be used to demonstrate the system performance. The use of CHAIN is shown to be capable of significantly reducing the filter failure rate from 44% to 14% when a small number of particles is used in the filter (250) and the initial position is poorly known.

Low Cost, High Accuracy Positioning In Urban Environments

It is well known that GPS measurements are regularly obstructed in urban environments. Positioning accuracy in such environments is significantly degraded and in many areas, it is not possible to obtain a GPS position fix at all. There are currently two methods that can be used to improve availability in the urban environment. Firstly, GPS receivers can be augmented with dead reckoning sensors such as an INS. Alternatively, High Sensitivity GPS (HSGPS) receivers can be used which are able to acquire and track very weak signals. This paper assesses the performance obtained from a GPS and low cost INS integrated system and a HSGPS receiver in an urban environment in Nottingham, UK. The navigation systems are compared to a high accuracy integrated GPS/INS system which is used to provide a reference trajectory. It is demonstrated that the differential GPS and low cost INS system can provide horizontal positioning accuracy of better than 2·5 m RMS in real-time, and better than 1 m RMS in post-processing, whereas the non-differential HSGPS receiver provides a real-time performance of 5 m RMS. These results were obtained in an environment where, with conventional GPS receivers, a position solution is only available 48·4% of the time. Operational considerations such as initial alignment of the GPS and low cost INS are also discussed when comparing the two systems for urban positioning applications.

The Potential Impact of GNSS/INS Integration on Maritime Navigation

A version of this paper was presented at ENC-GNSS 2007, Geneva. Its reproduction was kindly authorised by the ENC-GNSS 07 Paper Selection Committee. The General Lighthouse Authorities of the UK & Ireland commissioned an assessment of the impact that the integration of Global Navigation Satellite Systems (GNSS) with Inertial Navigation Systems (INS) would have on the aids to navigation (AtoN) services currently provided, and those to be provided in the future. There is concern about the vulnerability of GNSS, and the provision of complementary and backup systems is seen to be of great importance. The integration of INS could provide an independent and self-contained navigation system, for a limited time period, invulnerable to external intentional or unintentional interference, or the influences of changes in national policies. The study included an analysis of the potential use of GNSS-INS in three of the four phases of a vessels voyage: coastal, port approach and docking. The project consisted of a technology assessment, looking at the different inertial technologies that might be suitable for each phase. This was followed by a technology proving stage, evaluating suitable equipment using simulation and field trials to prove that the claimed performance could be achieved in practice. The final stage of the project was to assess the effects of the availability of such systems on existing and planned aids to navigation services.

A Multi-Sensor Navigation Filter for High Accuracy Positioning in all Environments

The aim of the SPACE project is to develop a mobile test bed that can position to within centimetres in all conditions and environments. To achieve this goal, a number of different positioning technologies have to be integrated together including GNSS, INS, pseudolites and other technologies such as Ultra Wideband and Bluetooth ranging. The integration of these sensors is achieved by the development of a ‘plug and play’ filter that will optimally combine measurements from each sensor to form an accurate position solution. The filter has been designed so that the sensors are integrated at the measurement level wherever possible, so partial measurements from different systems can be used together. This paper describes the development of the plug and play filter, focussing in particular on how the states are defined, how the measurements are used, and how a generic filter can be developed that can integrate all types of positioning sensor. Some early results from the filter are shown integrating GPS, INS and simulated measurements from an Ultra Wideband system. A prototype version of the mobile test bed is also described.

ROTATING A MEMS INERTIAL MEASUREMENT UNIT FOR A FOOT-MOUNTED PEDESTRIAN NAVIGATION

Pedestrian navigation especially indoors suffers from the unavailability of useful GNSS signals for positioning. Alternatively, a low-cost Inertial Measurement Unit (IMU) positioning system that does not depend on the GNSS signal can be used for indoor navigation. However its performance is still compromised because of the fast-accumulating heading drift error affecting such a low-cost IMU sensor. This results in a huge positioning error when navigating more than a few seconds using only the low-cost sensor. In this study, real field trials results are presented when a foot-mounted IMU is rotated on a single axis. Two promising results have been obtained. First, it mitigates the heading drift error significantly and second, it increases the observability of IMU z-axis gyro bias error. This has resulted in a greatly reduced error in position for the low-cost pedestrian navigation system.

Collaborative navigation field trials with different sensor platforms

Collaborative (or cooperative) positioning or navigation uses multiple location sensors with different accuracy on different platforms for sharing of their absolute and relative localizations. Typical application scenarios are dismounted soldiers, swarms of UAVs, team of robots, emergency crews and first responders. This paper studies the challenges to realize a public and low-cost solution, based on mass users of multiple-sensor platforms. For the investigation field experiments revolved around the concept of collaborative navigation in a week at the University of Nottingham in May 2012. Different sensor platforms have been fitted with similar type of sensors, such as geodetic and low-cost high-sensitivity GNSS receivers, tactical grade IMUs, MEMS-based IMUs, miscellaneous sensors, including magnetometers, barometric pressure and step sensors, as well as image sensors, such as digital cameras and Flash LiDAR, and ultra-wide band (UWB) receivers. The employed platforms in the tests include a train on a building roof, mobile mapping vans and personal navigators. The presented preliminary results of the field experiments show that a positioning accuracy on the few meter level can be achieved for the navigation of the different platforms.