UC Santa Cruz

Current smartphone-based localization systems, primarily designed towards sighted individuals, offer wayfinding services by tracking a user's path. However, this design overlooks the unique navigation needs of blind individuals who utilize long canes or guide dogs and have distinct movement patterns. To bridge this gap, this thesis introduces novel localization techniques tailored for blind pedestrians in both indoor and outdoor settings. These techniques avoid the need for BLE beacons and Wi-Fi, as well as camera-based systems, all of which are impractical for blind users. Instead of these options, the proposed methods utilize IMU sensors, allowing users to conveniently place their phones in their pockets without the requirement of any external infrastructure.

Indoor localization in the absence of maps is addressed in this thesis through a unique combination of a Mixture Kalman filter and a GRU-based straight walking detector. Together, these form a two-stage turn detector that operates under the assumption that corridor intersections occur at 45° or 90° angles. In situations where maps are accessible, the research incorporates two Pedestrian Dead Reckoning (PDR) methods with the map data via a particle filter. In outdoor settings, this thesis expands the use of IMU sensor data by integrating it with GPS signals through a particle filter. This method creates a flexible model effective in both open areas and in environments with wall constraints, as specified by maps. Comprehensive testing of these systems involved trials with the WeAllWalk dataset, containing data from visually impaired walkers, and user studies conducted using two separate iPhone applications for indoor and outdoor localization. Results from these tests clearly demonstrate the effectiveness of the proposed localization solutions.