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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Lightweight Stereo Vision for Obstacle Detection and Range Estimation in Micro-Mobility Vehicles.

Jiansheng Ruan¹, Hui Weng¹, Zhaojun Yuan¹

¹School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)

|March 28, 2026

Summary

This study introduces HAGVNet, a lightweight stereo matching network for accurate obstacle detection and range estimation in micro-mobility vehicles. It offers a practical solution for embedded systems with low power and computation budgets.

Keywords:

embedded deployment lightweight networks micro-mobility vehicles range estimation stereo matching stereo vision

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Area of Science:

Computer Vision
Robotics
Embedded Systems

Background:

Micro-mobility vehicles require efficient obstacle detection and range estimation in constrained environments.
Existing solutions often face limitations in cost, power, and computational resources for embedded applications.

Purpose of the Study:

To propose HAGVNet, a lightweight stereo matching network for embedded ranging.
To validate its deployability in a target-level ranging pipeline using YOLOv11n.
To achieve accurate distance and 3D position estimation for micro-mobility applications.

Main Methods:

HAGVNet utilizes a hierarchical attention-guided cost volume (HAGV) for modulated cost modeling.
Employs ConvNeXtV2-style 2D cost aggregation for enhanced stability and boundary consistency.
Integrates depth statistics within detected regions for target distance and 3D position estimation.

Main Results:

HAGVNet achieves 0.73 px EPE on SceneFlow with 20.08 G FLOPs, demonstrating an excellent accuracy-complexity trade-off.
On an embedded Jetson Orin Nano Super, it reaches 46.3 FPS (TensorRT FP16).
Field tests show 0.5-8.6% relative ranging errors within 2-10 m.

Conclusions:

HAGVNet provides a computationally efficient and accurate solution for embedded ranging.
Its performance validates practical feasibility for low-speed target-level ranging in micro-mobility.
The network offers a promising approach for real-world deployment under strict resource constraints.