6 accepted papers
Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers which however are not always grounded in the input image. We investigate this phenomenon usually referred to as "hallucination" and show that it stems from an excessive reliance on the language prior. I…
Decentralized visual simultaneous localization and mapping (SLAM) is a powerful tool for multi-robot applications in environments where absolute positioning is not available. Being visual, it relies on cheap, lightweight and versatile cameras, and, being decentralized, it does not rely on communicat…
Existing Simultaneous Localization and Mapping systems require an extensive manual pre-calibration process. Non-manual calibration procedures use manipulators to create known patterns in order to estimate the unknown calibration. Calibration is often time-consuming and involves humans performing rep…
We propose a distributed algorithm to estimate the 3D trajectories of multiple cooperative robots from relative pose measurements. Our approach leverages recent results [1] which show that the maximum likelihood trajectory is well approximated by a sequence of two quadratic subproblems. The main con
Large-scale SLAM demands for scalable techniques in which the computational burden and the memory consumption is shared among many processing units. While recent literature offers competitive approaches for scalable mapping, these usually involve approximations to preserve sparsity of the resulting…
In this paper, we present an information-based approach to select a reduced number of landmarks and poses for a robot to localize itself and simultaneously build an accurate map. We develop an information theoretic algorithm to efficiently reduce the number of landmarks and poses in a SLAM estimate…