Abstract To address central pixel dependency and degraded matching reliability in low-texture or occluded regions, this paper proposes a robust binocular ranging framework integrating an improved Census-BT cost with a multi-resolution ROI pyramid strategy. The approach employs an annular Gaussian-weighted Census transform combined with weighted BT costs to alleviate the limitations of traditional Census transforms, significantly enhancing robustness against noise and complex environments. To optimize computational efficiency for real-time applications, an enhanced YOLO11 model is integrated to generate region of interest (ROI) layers for cross-scale disparity refinement, achieving a 2.8% improvement in precision compared to the baseline. This localization facilitates progressive disparity refinement across pyramid layers, enabling reliable depth estimation without the extensive supervised retraining characteristic of learning-based models. Evaluated on benchmarks, the algorithm demonstrates a favorable accuracy and efficiency trade-off compared to traditional non-learning baselines, achieving an 11.33% D1-all error rate on KITTI 2015 and a minimum mismatch rate of 9.52% on Middlebury 2021 under challenging conditions. System-level validation using a ZED stereo camera substantiates the framework’s practical efficacy. In real-world ranging experiments across diverse scenarios, the system maintains a maximum relative ranging error of 3.19% with practical measurement accuracy consistently exceeding 96.81%. With an execution time below 0.75 seconds, the proposed framework achieves an effective balance between accuracy and processing speed, making it suitable for robotic perception and industrial object measurement on resource constrained edge devices.
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