Engineering Deep Dive How Multi-Sensor Fusion Solves the "Head-Height" Hazard Problem
Traditional electronic travel aids often rely solely on single-point ultrasonic transducers or visual cameras. Single-point ultrasound suffers from wide beam reflection loss on soft clothing or angled surfaces, while single RGB cameras struggle under rapid lighting changes or glare.
Sprhava’s proprietary Obstacle Mapping Architecture solves these limitations by implementing a tri-layer sensor fusion approach:
Layer 1: Solid-State Micro-LiDAR Array
Emits thousands of invisible 940nm laser pulses per second to build a millimeter-accurate point cloud of physical structures ahead. It measures precise distance to solid walls, glass panels, poles, and overhead obstacles completely independent of ambient lighting.
Layer 2: High-Frame-Rate RGB Stereo Vision Engine
Analyses visual scene context to perform real-time semantic segmentation. It categorizes detected objects into specific hazard classes (e.g., "Step Down", "Moving Pedestrian", "Glass Door", "Construction Cone") so the system can prioritize urgent safety warnings over static background geometry.
Layer 3: 6-Axis Inertial Measurement Unit (IMU) & Ego-Motion Logic
Tracks the wearer's head movement, pitch, and walking velocity. This prevents false positive alerts when the user tilts their head down to tied shoes or turns quickly in a crowded hallway, ensuring alerts are only triggered for genuine collision vectors.