Stereopsis
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Stereopsis is the computational process by which depth and three-dimensional structure are perceived by comparing slightly offset images captured from two or more cameras, mimicking the binocular vision of biological systems. In robotics and AI, stereo vision systems use triangulation — calculating disparity between corresponding points in left and right camera images — to generate dense depth maps and 3D point clouds of the surrounding environment. These depth estimates feed directly into core robotic capabilities including obstacle detection and avoidance, path planning, simultaneous localization and mapping (SLAM), object recognition, and autonomous navigation across ground robots, humanoids, UAVs, and planetary rovers. Stereopsis matters because it provides reliable metric depth information using passive, lightweight sensor hardware without requiring active illumination, making it practical across diverse environments from indoor warehouses to Martian terrain. Compared to monocular approaches, stereo vision delivers more accurate and robust distance measurements, while remaining complementary to other sensing modalities like lidar, GPS, and tactile sensing in multi-sensor robotic systems.
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