Real-Time LiDAR-Based Stair and Elevation Change Detection on Consumer Mobile Hardware: A Single-User Study

<title>Abstract</title> <p>For individuals who are blind or visually impaired (BVI), undetected stairways, curbs, and sudden elevation changes represent a leading cause of fall-related injury. Herein we present and evaluate a computationally efficient geometric algorithm for detecting stair and elevation changes in real-time implemented entirely on an LiDAR-equipped consumer smartphone (iPhone 17 Pro Max, iOS 26.2). The algorithm uses a set of eight downward- and forward-facing raycasts projected into an ARKit (Artificial Reality developer Kit)-reconstructed mesh and performs line-segment angular analysis at 60 Hz within a 16.7 ms per-frame budget, without requiring cloud processing, GPU inference, or custom hardware. A single-user evaluation across 120 trials spanning eight real-world hazard locations, five step heights (4.5 to 7.5 in), four stair grades (22 to 33 degrees), varied lighting conditions (indoor artificial, outdoor daytime, and outdoor nighttime), and both ascending and descending approaches yielded an overall detection rate of 92.5% with zero false positives and a mean alert distance of 2.31 ft (SD = 0.91 ft). Detection reliability generally increased with step height, with more consistent detection performance above six inches of step height. Descending approaches produced lower detection rates and shorter warning distances than ascending approaches, a limitation intrinsic to the geometry of depth-based sensing. These results demonstrate that accurate, low-latency elevation hazard detection is achievable on commodity mobile hardware, with implications for the design of accessible, low-cost assistive navigation tools.</p>