Pedestrian Navigation Method Research Articles

Two-mode navigation method for low-cost inertial measurement unit-based indoor pedestrian navigation

The traditional inertial navigation system-based indoor pedestrian navigation method is not suitable to all the conditions, since its data fusion model is designed only for a single condition. In order to achieve better performance, a two-mode navigation method for indoor pedestrian navigation is proposed in this work, which includes the stance mode and the swing mode. When the person’s foot is in a stance phase, the stance mode is used to correct the velocity error and yaw error. When the person’s foot is in a swing phase, the swing mode works and only the yaw error is able to be corrected. For verification, a real indoor test has been done to assess the performance of the proposed two-mode method. The position root-mean-square error (RMSE) value is 1.9265 m during a 176-m traverse, and the RMSE of yaw is 0.20734 rad. These results demonstrate that our method is effective to reduce the error compared with the conventional schemes.

Pedestrian navigation with high sensitivity GPS receivers and MEMS

Following a brief introduction to pedestrian navigation and dead-reckoning methods, the characteristics of the current global navigation satellite system (GNSS), namely the US global positioning system (GPS), and its suitability for pedestrian navigation are reviewed. Error sources affecting system performance under both line-of-sight and attenuated signal conditions are described. Attenuated signals can now be acquired and tracked under certain indoor environments, but accuracy and other performance measures are degraded. Pedestrian navigation examples under the forestry canopy and indoor are used to show not only performance but also performance variability as a function of the environment. The use of micro-electro-mechanical systems (MEMS) to aid GPS is introduced. Their advantages and limitations are described through the use of an urban canyon test in a city core, a very harsh RF environment due to signal multipath. It is found that although MEMS aiding substantially improve performance, test conditions have a major impact on actual performance. As a consequence, enough test results are not available at this time to fully characterize performance as a function of the environment due to the high variability of the latter. Accuracy, for instance, varied between 15 and 150 m in the few tests deported herein. Finally, a few predictions are made regarding potential future improvements.

Visual odometer for pedestrian navigation

This paper presents a visual odometer system using stereo cameras for pedestrian navigation. A novel method for pedestrian navigation based on the knowledge of gait analysis and robust ego-motion estimation is proposed. Two major problems of implementing the system on a pedestrian are stated. Firstly, the features collected from cameras attached on a walking pedestrian normally have winding trajectory resulting in inaccurate tracking. Secondly, the observed object moving independently leads to incorrect ego-motion estimation. Using gait analysis, capturing images at the same stage of the walking cycle produces a less winding trajectory that allows tracking without stabilizing the images. Robust ego motion is also introduced to eliminate outliers that are independently moving features, mismatched features in the stereo matching step and incorrectly assigned features in the tracking step. Data processing techniques including corner detection, stereo matching, triangulation, tracking, and ego-motion estimation are employed. The outcome is the estimated incremental ego motion of the stereo cameras. The approach not only enables the system to operate on walking users but also improves the accuracy of ego-motion estimation.