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Related Experiment Video

Updated: Apr 5, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

VLM-Nav: Mapless UAV navigation using monocular vision driven by vision-language models.

Gobinda Chandra Sarker^1,2, Akm Azad³, Sejuti Rahman^2,4

¹Department of Electrical and Computer Engineering, University at Albany, Albany, New York, United States of America.

|April 3, 2026

Summary

This summary is machine-generated.

This study introduces a novel vision-based navigation system for Unmanned Aerial Vehicles (UAVs) that uses depth estimation and Vision-Language Models (VLMs) for efficient obstacle avoidance and path planning.

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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

Area of Science:

Robotics and Autonomous Systems
Computer Vision
Artificial Intelligence

Background:

Autonomous vehicles, particularly Unmanned Aerial Vehicles (UAVs), require advanced navigation for diverse applications like delivery and surveillance.
Current navigation systems often rely on expensive sensors (e.g., LiDAR) or pre-existing maps, limiting their cost-effectiveness and adaptability.

Purpose of the Study:

To develop a novel, cost-efficient, and generalizable vision-based navigation method for UAVs.
To integrate depthmap estimation with Vision-Language Models (VLMs) for real-time obstacle avoidance and path planning.
To demonstrate the system's effectiveness in complex, dynamic, and unfamiliar environments without relying on external sensors or maps.

Main Methods:

Utilized DepthAnything-V2 for zero-shot depthmap estimation from RGB images captured by the UAV.
Integrated Vision-Language Models (Gemini-flash, GPT-4o) to analyze depthmaps, detect obstacles, and plan avoidance maneuvers.
Employed a fully connected network to combine VLM outputs with UAV heading for optimal course prediction.

Main Results:

Simulations in AirSim (Blocks, Downtown West environments) demonstrated consistent UAV navigation to destinations.
The system achieved a near-perfect task completion rate of 0.98, effectively avoiding obstacles.
Validated the feasibility of a sensor- and map-independent navigation solution.

Conclusions:

The proposed vision-based navigation method offers a cost-efficient and generalizable approach for UAVs in complex environments.
Highlights the potential of integrating depth estimation with VLMs for advanced autonomous navigation.
Paves the way for emerging trends in autonomous systems research leveraging multimodal AI models.