可变分辨率虚拟地图用于无人水面舰艇自主探索
Variable-Resolution Virtual Maps for Autonomous Exploration with Unmanned Surface Vehicles (USVs)
- Department of Mechanical Engineering, University College London, London, UK（伦敦大学学院机械工程系）
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA（新泽西州霍博肯斯坦福理工学院机械工程系）
- Department of Computer Science, Dartmouth College, Hanover, NH, USA（达特茅斯学院计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出可变分辨率虚拟地图方法，通过自适应四叉树和双变量高斯虚拟地标，有效解决GNSS退化环境下自主探索中的定位和地图不确定性问题，提升计算效率和安全性。
AI中文摘要：
无人水面舰艇（USVs）在近岸水域的自主探索需要可靠的定位和扩展区域的一致制图，但GNSS退化、环境引起的定位不确定性以及有限的 onboard 计算能力构成了挑战。基于虚拟地图的方法通过紧密耦合因子图SLAM与地图不确定性准则来显式建模定位和制图不确定性。然而，其存储和计算成本与固定分辨率工作空间离散化呈不良比例关系，导致在大型近岸环境中效率低下。此外，过度重视特征稀疏的开阔水域区域可能增加SLAM失败的风险，由于探索与利用之间的失衡。为了解决这些限制，我们提出了一种可变分辨率虚拟地图（VRVM），这是一种计算高效的表示地图不确定性的方法，使用双变量高斯虚拟地标放置在自适应四叉树的单元格中。自适应四叉树使地图不确定性表示具有面积加权特性，保持粗略、远距离的虚拟地标有意不确定，同时在信息密集区域分配更高分辨率，并减少地图估值对树局部细化的敏感性。采用期望最大化（EM）规划器来评估使用VRVM的前沿姿态和地图不确定性，平衡探索与利用。我们在VRX Gazebo仿真器中评估VRVM与几种最先进的探索算法，在不同测试场景中使用现实的码头环境，随着探索难度的增加。结果表明，我们的方法在GNSS退化的近岸环境中提供了更安全的行为和更有效的 onboard 计算利用。
英文摘要：
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.
