SurveilNav: Collaborative Object Goal Navigation with Robot and Surveillance System
A novel dataset and framework, SurveilNav, enables robots to collaborate with multi-view surveillance systems for object goal navigation. By integrating active camera scheduling, joint 2D/3D mapping, VLM-based value estimation, and collaborative target verification, it overcomes the limitations of single-robot perception and fixed-camera blind spots. Experiments on HM3D demonstrate state-of-the-art performance in exploration efficiency and navigation success rate.
[2606.25119] SurveilNav: Collaborative Object Goal Navigation with Robot and Surveillance System
[Submitted on 23 Jun 2026]
Title:SurveilNav: Collaborative Object Goal Navigation with Robot and Surveillance System
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Abstract:With the growing deployment of surveillance systems in factories, offices, and homes, integrating them with robots offers a promising direction for collaborative and efficient task execution. However, existing approaches largely focus on single-robot scenarios and struggle with multi-view collaboration in large-scale environments. In this paper, we present a novel indoor collaborative object navigation dataset built on Habitat-Sim, featuring 206 cameras across 74 floors. The dataset enables systematic evaluation of an agent's ability to exploit multi-view surveillance information. To address the limitations of single-robot perception, we propose SurveilNav, a collaborative navigation framework that integrates active camera scheduling, joint 2D/3D mapping, VLM-based value estimation, and collaborative target verification. By synergizing the robot's dynamic local perception with the static global view of surveillance, this architecture effectively overcomes both the limited perception range of single agents and the inherent blind spots of fixed cameras, resolving inefficient exploration. Experimental results on the HM3D dataset demonstrate that SurveilNav substantially outperforms existing methods, achieving state-of-the-art performance in both exploration efficiency and navigation success rate. Moreover, the system shows strong potential for applications in large-scale search, home environments, and rescue missions.
Comments: Accepted by ICRA 2026
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.25119 [cs.RO]
(or arXiv:2606.25119v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.25119
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ming-Ming Yu [view email] [v1] Tue, 23 Jun 2026 19:45:44 UTC (7,860 KB)
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