# OpenBench: A New Benchmark and Baseline for Semantic Navigation in Smart Logistics

arXiv: https://arxiv.org/abs/2502.09238 · HTML: https://mlsanity.com/paper/2502.09238/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [CostNav: A Navigation Benchmark for Real-World Economic-Cost Evaluation of Physical AI Agents](https://mlsanity.com/paper/2511.20216/) (2025)
- [G-DRAGON: Geospatial Reasoning and Dynamic Planning for Retrieval-Augmented Outdoor Navigation](https://mlsanity.com/paper/2605.25646/) (2026)
- [Event-Adaptive Motion Planning with Distilled Vision-Language Model in Safety-Critical Situations](https://mlsanity.com/paper/2606.25629/) (2026)
- [Physico-Geospatial Grounded Scene Interpretation for Mobile Robotics](https://mlsanity.com/paper/2609.06629/) (2026)

## Abstract (arXiv)

The increasing demand for efficient last-mile delivery in smart logistics underscores the role of autonomous robots in enhancing operational efficiency and reducing costs. Traditional navigation methods, which depend on high-precision maps, are resource-intensive, while learning-based approaches often struggle with generalization in real-world scenarios. To address these challenges, this work proposes the Openstreetmap-enhanced oPen-air sEmantic Navigation (OPEN) system that combines foundation models with classic algorithms for scalable outdoor navigation. The system uses off-the-shelf OpenStreetMap (OSM) for flexible map representation, thereby eliminating the need for extensive pre-mapping efforts. It also employs Large Language Models (LLMs) to comprehend delivery instructions and Vision-Language Models (VLMs) for global localization, map updates, and house number recognition. To compensate the limitations of existing benchmarks that are inadequate for assessing last-mile delivery, this work introduces a new benchmark specifically designed for outdoor navigation in residential areas, reflecting the real-world challenges faced by autonomous delivery systems. Extensive experiments in simulated and real-world environments demonstrate the proposed system's efficacy in enhancing navigation efficiency and reliability. To facilitate further research, our code and benchmark are publicly available.