Achieving long-range, high-accuracy depth perception under stringent power constraints remains a critical challenge for stereo vision in edge applications. This work presents a cascadable stereo matching processor that overcomes the inherent trade-off between sensing range and computational efficiency. The core innovation is a scalable semi-global matching (SSGM) algorithm which dynamically optimizes the disparity search range for different baselines, ensuring constant on-chip memory usage and a significant reduction in data movement. The architecture further integrates a raw-domain rectification front-end, which performs direct geometric transformation on Bayer-patterned image streams. This approach eliminates the need for external memory access by bypassing conventional ISP pipelines, thereby maximizing throughput and reducing system memory consumption. Parallel processing paths for multiple baselines converge in a pixel-wise fusion module, which synthesizes a unified depth map by selecting the most reliable disparity estimate for each output pixel. The cascadable stereo matching processor achieves speedups of up to 178x and 97x over CPU and EdgeGPU platforms, respectively, in multi-baseline stereo disparity fusion. Implemented in 40-nm CMOS technology, the processor operates at 160 MHz, achieving a processing speed of 80 frames per second with an energy efficiency of 7.9 pJ/pixel and occupying a core area of 6.04 mm².