Accuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development

Goal:
Distance information is highly requested in assistive smartphone Apps by people who are blind or low vision (PBLV). However, current techniques have not been evaluated systematically for accuracy and usability.
Methods:
We tested five smartphone-based distance-estimation approaches in the image center and periphery at 1–3 meters, including machine learning (CoreML), infrared grid distortion (IR_self), light detection and ranging (LiDAR_back), and augmented reality room-tracking on the front (ARKit_self) and back-facing cameras (ARKit_back).
Results:
For accuracy in the image center, all approaches had <±2.5 cm average error, except CoreML which had ±5.2–6.2 cm average error at 2–3 meters. In the periphery, all approaches were more inaccurate, with CoreML and IR_self having the highest average errors at ±41 cm and ±32 cm respectively. For usability, CoreML fared favorably with the lowest central processing unit usage, second lowest battery usage, highest field-of-view, and no specialized sensor requirements.
Conclusions:
We provide key information that helps design reliable smartphone-based visual assistive technologies to enhance the functionality of PBLV.