← Back to all news
arXiv Computer Vision By Lkhanaajav Mijiddorj, Yang Yan, Tyler Beringer, Bilguunzaya Mijiddorj, Alex N. Ho, Bin Xu, Binbin Weng

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

Read the original on arXiv Computer Vision →

The paper presents a lightweight monocular vision pipeline for extracting sidewalk paths on low‑power embedded micromobility platforms. It evolves through three design iterations—from a skeleton‑graph baseline to a distance‑transform corridor planner and finally to a compact image‑space architecture—using a SegFormer‑B0 student model trained with semi‑supervised pseudo‑labels. The final system achieves high segmentation accuracy (IoU 0.946) and fast planning (under 50 ms per frame) while reducing temporal instability and increasing template‑path availability across real campus sequences.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
6d ago

Learning to Navigate with Minimal Parameters: Decomposing Visual Navigation Through Closed-Form Geometric Interfaces

The paper introduces a compact visual navigation system that decomposes the task into three analytically‑computed geometric interfaces and three small learned modules: an egress predictor, a navigation predictor, and an endpoint‑pinned residual diffusion generator. Only 0.58 M of the 23 M parameters are trained on 44 k frames, achieving competitive success rates and the lowest collision rate among evaluated methods across 6 060 point‑goal episodes in 60 environments. The design allows further parameter reduction by replacing the frozen image encoder with a 0.54 M MobileNetV2, supports zero‑shot deployment on a Jetson Orin Nano UGV, and enables transparent failure analysis under sensor corruption.

By Edward Beng Wai Tan, Siew-Kei Lam
diffusion
More like this →
arXiv Computer Vision
Sep 16

EgoPathBench: Evaluating Zero-Shot Egocentric Waypoint Decision-Making in Vision-Language Models

EgoPathBench is a new dataset and benchmark that tests zero‑shot egocentric waypoint decision‑making in vision‑language models. Each task presents an egocentric RGB image, a natural‑language goal, and numbered visible waypoints, and models must return traversable candidates or an ordered route. The benchmark evaluates candidate feasibility, edge legality, and goal arrival under point‑agent or embodied geometry, covering 31,852 training, 1,345 validation, and 1,111 benchmark questions. "whyItMatters":"The benchmark reveals that current VLMs perform poorly on integrated navigation tasks, highlighting a gap in spatial intelligence that can be addressed by fine‑tuning with the released training data."

By Yang Zhao, Zhuo Chen, Xubo Yang
llmsagentsroboticsfine-tuningmultimodalbenchmarks
More like this →