This paper considers the problem of increasing the accuracy of object distance determination in a video-centric self-organizing multi-robot system without odometry (POM-MRS) operating in a quasi-stationary mode. In modern POM-MRS, the tasks of detecting a reference agent for measuring the distance to an environmental object, network identification of the reference agent (establishing a correspondence between the agent’s image and its network address), and determining its orientation are solved using complex neural network detection. This limits the distributed stereo basis and results in relatively low accuracy of object distance measurements in large-scale open spaces. To improve the accuracy of environmental distance determination, we propose a model based on local consistent triangulation with an analytical assessment of measurement errors and protocol-geometric verification of agent detection hypotheses. The model reduces these errors compared to single-robot system models based on odometry and existing POM-MRS models based on complex neural network agent detection.

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