VLM-Nav Configurations for UAV Navigation in AirSim Environments

Gobinda Chandra Sarker's dataset, last updated in April 2026, contains configuration data for a vision-based UAV navigation system. The 5.5 KB dataset, stored in XLS format, relates to a method integrating depthmap estimation with Vision-Language Models for obstacle avoidance and path planning. The system's performance was validated in AirSim simulations using the Blocks and Downtown West environments.

Use Cases

• Benchmarking vision-language models (Gemini-flash, GPT-4o) for UAV navigation tasks based on the described integration method.
• Analyzing system performance metrics like task completion rate based on the reported 0.98 success rate.
• Comparing cost-efficient navigation approaches that eliminate LiDAR and pre-existing maps as described in the study.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• System achieved a high task completion rate of 0.98 in simulations as stated in the description.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

Gobinda Chandra Sarker via figshare

Collection Method

Likely generated from simulations of a proposed UAV navigation system integrating depth estimation and VLMs.

Time Range

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Freshness

Last updated 2026-04-03 17:54:44; freshness should be verified.

Geography

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