Traversability prediction is a core component of autonomous navigation, yet most vision-based methods operate frame-wise and produce temporally inconsistent predictions. We present SAM-VTP (Video Traversability Prediction), which fine-tunes SAM2's video memory pathway for dense, prompt-free traversability segmentation, conditioning each frame on a persistent memory of prior observations. Rather than inferring terrain geometry from appearance, we measure it: slope, roughness and height are computed from depth and fused into the image features. We also introduce an offline continuous supervision pipeline that generates dense per-pixel traversability targets in [0,1] from depth-derived geometry and semantic priors, requiring no robot deployment and no per-pixel annotation, capturing conditional feasibility that binary labels cannot represent. On TartanGround, a large-scale synthetic ground-robot dataset, SAM-VTP improves fuzzy Jaccard from 0.908 to 0.924 and binary IoU from 0.854 to 0.886 over SAM-TP, a frame-wise SAM2 baseline, against a seed-to-seed spread below 0.001, and cuts frame-to-frame flicker by 31%. In closed-loop trials at two field sites, driving the same planner and controller, the baseline's dropouts leave the planner without a feasible path 3.3x more often on a paved course and 6.9x on rough ground, while SAM-VTP runs at 21.4 FPS on the Orin Nano the robot carries.

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