Binocular vision systems employ the principle of triangulation to determine spatial target pose parameters, which has become a fundamental solution for robotic vision-based navigation and localization under unstructured environments. This chapter systematically investigates the image preprocessing pipeline in binocular vision, specifically addressing image background subtraction through differential computation and color-based filtering techniques. The stereo matching process for binocular vision is elucidated in a theoretical–experimental framework, involving matching cost calculation, matching cost aggregation, disparity estimation, and disparity refinement. The 3D point cloud generation and processing procedure is further summarized, therefore verifying the effectiveness of point cloud registration and optimization. Finally, a robotic vision guidance strategy is introduced, which can estimate target pose changes from multi-view 3D point clouds.