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arXiv · 2606.26046

RoboAtlas: Contextual Active SLAM

Alexander Schperberg·Shivam K. Panda·Abraham P. Vinod·Rokaha Bhagawan·M. K. Jawed·Stefano Di Cairano
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Abstract

We present RoboAtlas, a contextual Active SLAM framework that adaptively balances geometric exploration and semantic reasoning using a scalable 3D semantic mapping system, OpenRoboVox. RoboAtlas integrates frontier exploration, global semantic-map reasoning, and egocentric VLM-based reasoning through a contextual multi-armed bandit that transitions from exploration to semantically guided navigation as scene understanding improves. We evaluate the system in simulation and on a Unitree Go2 robot in large-scale real-world environments exceeding 1800 m2 with approx. 30k mapped semantic instances, achieving a 100% task success rate. On the GOAT-Bench "Val Unseen" benchmark, RoboAtlas achieves state-of-the-art performance with highest reported success rate (SR) of 90.6%, using GPT-4o, improving over the strongest prior baseline by 17.8 percentage points in SR. Using the much smaller Qwen2.5-VL-7B model, it still achieves 88.8% SR, outperforming all baselines using GPT-4o in SR, and revealing the importance of the information gained by our semantic mapping framework over simply replacing the underlying foundation model. The results demonstrate that grounding foundation models with large-scale 3D semantic maps enables robust and efficient contextual Active SLAM.

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Alexander Schperberg, Shivam K. Panda, Abraham P. Vinod, Rokaha Bhagawan, M. K. Jawed, Stefano Di Cairano. 2026-06-24. RoboAtlas: Contextual Active SLAM. https://arxiv.org/abs/2606.26046

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