Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.22667v1</id>\n    <title>Variable-Resolution Virtual Maps for Autonomous Exploration with Unmanned Surface Vehicles (USVs)</title>\n    <updated>2026-03-24T00:38:29Z</updated>\n    <link href='https://arxiv.org/abs/2603.22667v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.22667v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Autonomous exploration by unmanned surface vehicles (USVs) in near-shore waters requires reliable localisation and consistent mapping over extended areas, but this is challenged by GNSS degradation, environment-induced localisation uncertainty, and limited on-board computation. Virtual map-based methods explicitly model localisation and mapping uncertainty by tightly coupling factor-graph SLAM with a map uncertainty criterion. However, their storage and computational costs scale poorly with fixed-resolution workspace discretisations, leading to inefficiency in large near-shore environments. Moreover, overvaluing feature-sparse open-water regions can increase the risk of SLAM failure as a result of imbalance between exploration and exploitation. To address these limitations, we propose a Variable-Resolution Virtual Map (VRVM), a computationally efficient method for representing map uncertainty using bivariate Gaussian virtual landmarks placed in the cells of an adaptive quadtree. The adaptive quadtree enables an area-weighted uncertainty representation that keeps coarse, far-field virtual landmarks deliberately uncertain while allocating higher resolution to information-dense regions, and reduces the sensitivity of the map valuation to local refinements of the tree. An expectation-maximisation (EM) planner is adopted to evaluate pose and map uncertainty along frontiers using the VRVM, balancing exploration and exploitation. We evaluate VRVM against several state-of-the-art exploration algorithms in the VRX Gazebo simulator, using a realistic marina environment across different testing scenarios with an increasing level of exploration difficulty. The results indicate that our method offers safer behaviour and better utilisation of on-board computation in GNSS-degraded near-shore environments.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n    <published>2026-03-24T00:38:29Z</published>\n    <arxiv:primary_category term='cs.RO'/>\n    <author>\n      <name>Ye Li</name>\n    </author>\n    <author>\n      <name>Yewei Huang</name>\n    </author>\n    <author>\n      <name>Wenlong GaoZhang</name>\n    </author>\n    <author>\n      <name>Alberto Quattrini Li</name>\n    </author>\n    <author>\n      <name>Brendan Englot</name>\n    </author>\n    <author>\n      <name>Yuanchang Liu</name>\n    </author>\n  </entry>"
}