Lightweight Machine Learning-Driven Monocular Sidewalk Path Extraction for Embedded Micromobility Navigation

Researchers present a lightweight monocular vision pipeline for sidewalk path extraction in micromobility systems, achieving a hand-annotated IoU of 0.946 at 11.7 ms per frame with a compact SegFormer-B0 student model trained via a semi-supervised teacher-student framework using OneFormer Swin-L pseudo-labels. In a controlled comparison on 32 hand-labeled frames, image-space midpoint planning achieved the lowest lateral center error (14.3 px) at 2.2 ms, a 421x speedup over BEV distance-transform planning (926.8 ms, 65.0 px center error), while maintaining comparable mask-path alignment (98.5% versus 98.6%). The recommended architecture runs the full perception-to-path stack in under 50 ms per frame on CPU, suitable for embedded pedestrian-speed micromobility systems.

arXiv:2608.25178v1 Announce Type: new Abstract: Sidewalk-scale path extraction demands perception and planning that run reliably on compact, low-power hardware in cluttered, map-sparse environments. We present a monocular vision pipeline for sidewalk path extraction in micromobility systems that progresses through three design iterations, from a skeleton-graph baseline through distance-transform corridor planning to a lightweight image-space architecture, and provides a systematic comparison of five path-planning methods across both bird's-eye-view BEV and image-space domains. A compact SegFormer-B0 student model, trained with a semi-supervised teacher-student framework using OneFormer Swin-L pseudo-labels, achieves a hand-annotated IoU of 0.946 at 11.7 ms per frame, improving over the baseline checkpoint IoU 0.758, 18.9 ms . In a controlled planner comparison on 32 hand-labeled frames, image-space midpoint planning achieves the lowest lateral center error 14.3 px at 2.2 ms, a 421x speedup over BEV distance-transform planning 926.8 ms, 65.0 px center error , while maintaining comparable mask-path alignment 98.5% versus 98.6% . A full-video replay across six campus sequences 22,679 frames confirms that the improved segmentation reduces temporal instability from 1.46% to 0.33% and increases template-path availability from 73.7% to 79.3%. We further show that BEV-only path extraction is fragile in monocular settings: in one profiled run, 99.3% of frames produced no valid BEV path. The final recommended architecture, image-space midpoint primary, image-space distance-transform fallback, and BEV reserved for visualization, runs the full perception-to-path stack in under 50 ms per frame on CPU, making it suitable for embedded pedestrian-speed micromobility systems.