UC Irvine

This Ph.D. dissertation presented the research on development of pedestrian inertial navigation systems that track a person in a self-contained and infrastructure-free manner. The investigated approach utilized Zero-velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS) based on foot-mounted Inertial Measurement Units (IMUs). Prior implementations of the ZUPT-aided INS suffer from error sources originating from motion sensors, algorithm, and estimation. This dissertation attempted to address the identified error sources and proposed new algorithmic and system-level approaches. The main contributions of this thesis include: 1) approaches, on both algorithm and system levels, to address issues of insufficient inertial sensors’ Full-Scale Range (FSR) and bandwidth when mounting on the foot. On the algorithm level, a reconstruction filter was developed, predicting accelerometers’ signals exceeding measurable ranges. On the system level, an IMU array with a prioritization mechanism was developed, enabling measurements of acceleration as large as 200 [g]. The two approaches were experimentally verified to improve the positioning accuracy of a ZUPT-aided INS in cases of foot-mounted sensors experiencing large shocks; 2) a neural network-based approach to predict and compensate 12 different thermal-induced errors, consisting of bias and noise variations in a 6-Degree of Freedom (DoF) IMU, based on temperatures and temperature rates. It was experimentally verified that the developed temperature compensation approach reduced navigation error of ZUPT-aided INS by more than 15×, as compared to a traditional ZUPT-aided INS, in a scenario where ambient temperature varied with a range of 30◦C; 3) improved traditional IMU-based stance phase detection by fusing measurements of a foot-mounted IMU and a downward-facing ultrasonic sensor. The ultrasonic sensor was demonstrated with preferred properties that its measurements had a unique value only when the foot is in contact with the ground and the value does not vary in different pedestrian activities. It was demonstrated, with experiments involving walking and running, that the accuracy of the developed stance phase detector outperformed the traditional IMU-based detector and improved navigation accuracy of the ZUPT-aided INS by more than 2×; 4) an adaptive mechanism to vary the covariance of the zero-velocity measurements based on log-likelihood ratio metrics derived from measurements of a foot-mounted IMU. It was demonstrated that the adaptive covariance could bypass the necessity of using a stance phase detector in a ZUPT-aided INS. Results collected from indoor navigation experiments showed that the navigation accuracy of the ZUPT-aided INS using the developed adaptive covariance mechanism improved horizontal and vertical position accuracy by 36% and 64%, respectively, as compared to the case of conventional ZUPTaided INS using a stance phase detector; 5) a vision-based foot-to-foot positioning augmentation to increase the observability of the Extended Kalman Filter (EKF) on the yaw angle state in a dual foot-mounted IMU framework. This approach utilized a camera mounted on one foot to capture images of a feature pattern mounted on the other foot, and relative positions between the two feet were derived from the images. Numerical simulation and experimental results showed that the ZUPT-aided INS improved navigation accuracy by over 90% and 85%, respectively, as compared to the traditional ZUPT-aided INS. 6) developed and demonstrated, for the first time, a hybrid barometric/ultrasonic altimeter that considered a downward-facing ultrasonic sensor was mounted on foot to improve a ZUPT/Altimeter-aided INS framework, minimizing sensitivity of altitude measurements to variations in ambient temperature and air pressure. Experimental results showed that, in the case of abrupt temperature changes, the vertical displacement accuracy of ZUPT-aided INS augmented with the developed hybrid altimeter had a 96% and a 97% improvement, as compared to a standalone ZUPT-aided INS and aZUPT-aided INS augmented with a barometer, respectively. 7) a Simultaneously Localization And Mapping (SLAM) framework based on foot-mounted IMUs and environmental-deployed UWB beacons, referred to as UWB-Foot-SLAM, where beacons used did not need to be pre-deployed and pre-surveyed but were distributed in an environment during a navigation task and provided position compensation to bound position error growth. Additionally, this thesis developed the UWB-Foot-SLAM2 algorithm that augmented the original UWB-Foot-SLAM with self-contained aiding techniques. Experimental results showed that the developed UWB-Foot-SLAM framework mapped unknown beacons with displacement errors less than 0.5 [m]. In an indoor navigation experiment involving a pedestrian traveling around 3.5 [km] for an hour in a three-floor 50 [m] ×15 [m] ×15 [m] building on terrains of flat planes, ramps, stairs, and elevators, the 3D loop-closure error of the UWB-Foot-SLAM2 algorithm was 0.62 [m].