Variable-Resolution Virtual Maps for Autonomous Exploration with Unmanned Surface Vehicles (USVs)

2026-03-23

AI Summary

This work addresses the challenges of simultaneous localization and mapping (SLAM) in nearshore environments where GNSS signals degrade and computational resources are limited. Traditional fixed-resolution virtual maps often suffer from information sparsity, leading to SLAM failure and inefficiency. To overcome this, the authors propose a variable-resolution virtual map (VRVM) framework that adaptively allocates resolution via a quadtree structure—modeling high-information-density regions with fine granularity while coarsely representing distant areas. The approach incorporates bivariate Gaussian virtual landmarks to encode map uncertainty and integrates factor graph SLAM with an expectation-maximization (EM)-based planner to balance exploration and exploitation. Notably, an area-weighted uncertainty representation is introduced to reduce sensitivity to local over-refinement. Simulations in multi-difficulty dock scenarios from the VRX Gazebo benchmark demonstrate that VRVM significantly enhances SLAM robustness, computational efficiency, and navigation safety.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• autonomous exploration
• unmanned surface vehicles
• localisation uncertainty
• map representation
• GNSS-degraded environments

Innovation

• Variable-Resolution Virtual Map
• adaptive quadtree
• uncertainty-aware SLAM
• autonomous exploration
• unmanned surface vehicles