Performance comparison of wearable-based pedestrian navigation systems in large areas

Wearable devices are a key driver for the development of pedestrian navigation systems. In this work, we consider inertial navigation systems (INSs). There is a diversity of such INSs. Normally, the comparison of INSs is restricted to indoor environments, or to outdoor small areas. However, it is of interest to study the behaviour of INSs in large areas. To that end, we present a ground truth system with cm accuracy to evaluate navigation systems. The ground truth system is distributed in an area of 14380m2 approximately. The ground truth system is used to evaluate three INSs based on three different body locations: the thigh, which is denoted as pocket, the wrist and the foot. Additionally, the data from a glasses-mounted inertial measurement unit (IMU) are also collected. The data, as well as the ground truth, have been made available for download. The results of evaluating 995 ground truth points indicate that the foot INS outperforms the pocket INS in, at most, 2cm/s. The pocket INS has, in contrast, a better standard deviation of the position error, and a robust step detection. The wrist INS is the most sensitive system to outliers. Therefore, its average position error is the highest. All in all, there is still room for improvement in the performance of all evaluated INSs.

[1]  Estefania Munoz Diaz Inertial Pocket Navigation System: Unaided 3D Positioning , 2015, Sensors.

[2]  P. Robertson,et al.  Unscented Kalman filter and Magnetic Angular Rate Update (MARU) for an improved Pedestrian Dead-Reckoning , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[3]  R. Ying,et al.  Investigating the use of MEMS Based Wrist-worn IMU for Pedestrian Navigation Application , 2013 .

[4]  J. Ruppelt,et al.  High-precision and robust indoor localization based on foot-mounted inertial sensors , 2016, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[5]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[6]  Oliver J. Woodman,et al.  An introduction to inertial navigation , 2007 .

[7]  Estefania Munoz Diaz,et al.  Standalone inertial pocket navigation system , 2014, 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014.

[8]  Asier Perallos,et al.  Enhancing improved heuristic drift elimination for step-and-heading based pedestrian dead-reckoning systems , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[9]  Rui Cai,et al.  Evaluation of an autonomous navigation and positioning system for IAEA safeguards inspectors , 2014, 2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS).

[10]  Estefania Munoz Diaz,et al.  Step detector and step length estimator for an inertial pocket navigation system , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[11]  Fabian de Ponte Müller,et al.  Evaluation of AHRS algorithms for inertial personal localization in industrial environments , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[12]  Meng Zhang,et al.  Personal Dead Reckoning Using IMU Mounted on Upper Torso and Inverted Pendulum Model , 2016, IEEE Sensors Journal.

[13]  Laurent Itti,et al.  Walking compass with head-mounted IMU sensor , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Susanna Kaiser,et al.  Performance comparison of foot- and pocket-mounted inertial navigation systems , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).