## 2 Navigation System for Visually Impaired Pedestrians
### Importance of GNSS Positioning  
Precise and reliable positioning is required to support safe navigation services for persons with BLV. GNSS provides continuous positioning in the global frame in absolute coordinates. However, the performance of GNSS navigation in the urban environment is unsatisfactory. GNSS signals can be blocked or reflected over buildings, resulting in non-line-of-sight (NLOS) reception and the multipath effect [9]. These errors become more severe in highly urban cities with many high-rise buildings, such as Hong Kong and New York. As a result, researchers are trying to improve positioning by integrating different aids, such as inertial measurement units (IMU) and cameras.  
One of the most frequently used approaches to integrating the GNSS with IMU is pedestrian dead reckoning (PDR) [10]. The integration of GNSS and IMU takes advantage of two approaches. GNSS positioning can provide absolute coordinates in a global frame. However, the availability is limited by the number of visible satellites. The IMU can provide continuous positioning without subjection to external factors. However, it can only offer relative incremental coordinates in the local frame. At the same time, IMUs suffer from a continuous bias that accumulates over time. As a result, GNSS/INS integration can provide a continuous positioning with absolute coordinates.  
A camera is another popular aid for an integrated positioning system, which can provide the receiver's orientation in the local frame. Visual odometry (VO) [11] can extract the features from the image and estimate the relative orientation change of the receiver. VO assumes that feature points are static. Matching two consecutive frames can aid positioning by providing relative position. However, VO is sensitive to illumination conditions and feature availability. In addition, because VO assumes that feature points are static, dynamic objects highly affect performance. When the feature points move in parallel with the camera, the system may think it remains static itself. Therefore, research indicates tightly coupled VO with INS improves positioning performance [12].  
Research also suggests using a sky-pointing fisheye camera to detect the NLOS signal via image recognition algorithms [13; 14]. Therefore, only the predicted healthy satellites are utilized for GNSS positioning. The limitation of excluding unhealthy satellites is that when many high-rise buildings surround the receiver, exclusion can result in bad satellite geometry and degrading performance.  
Besides the aid of external sensors, improving the positioning accuracy of GNSS itself can definitely benefit the whole positioning system. Therefore, researchers are trying to identify and mitigate the NLOS error to improve the GNSS positioning alone. The consistency-check method [15] can detect and isolate unhealthy measurements and positioning performance can be improved to a satisfactory level. However, a consistency check will fail if the unhealthy measurements dominate the healthy ones [16].  
Dual-frequencies measurements also demonstrate good performance in mitigating the multipath effects and isolating NLOS errors. Researchers used the nature of the higher resolution of L5-band measurements to design a new weighting scheme; the weighted least squares (WLS) method has been demonstrated to improve performance [17].