This work addresses the problem of online exploration and visual sensor coverage of unknown environments. We introduce a novel perception roadmap we refer to as the Active Perception Network (APN) that serves as a hierarchical topological graph describing how to traverse and perceive an incrementally built spatial map of the environment. The APN state is incrementally updated to expand a connected configuration space that extends throughout as much of the known space as possible, using efficient difference-awareness techniques that track the discrete changes of the spatial map to inform the updates. A frontier-guided approach is presented for efficient evaluation of information gain and covisible information, which guides view sampling and refinement to ensure maximum coverage of the unmapped space is maintained within the APN. The updated roadmap is hierarchically decomposed into subgraph regions which we use to facilitate a non-myopic global view sequence planner. A comparative analysis to several state-of-the-art approaches was conducted, showing significant performance improvements in terms of total exploration time and surface coverage, and demonstrating high computational efficiency that is scalable to large and complex environments.

翻译：本工作针对未知环境的在线探索与视觉传感器覆盖问题展开研究。我们提出一种新型感知路线图——主动感知网络（Active Perception Network, APN），该网络作为分层拓扑图，描述了如何在逐步构建的环境空间地图中进行遍历与感知。APN状态通过增量更新扩展连通构型空间，使其尽可能延伸至已知空间的各个区域。为此，我们采用高效差分感知技术跟踪空间地图的离散变化，并基于这些变化指导状态更新。进一步，我们提出前沿引导方法对信息增益与共视信息进行高效评估，从而指导视点采样与优化，确保APN中未映射空间的最大覆盖。更新后的路线图被分层分解为子图区域，用于支持非短视全局视点序列规划。通过与多种先进方法的对比分析，本方法在总探索时间与表面覆盖率方面展现出显著性能提升，并证明了其对大规模复杂环境的高计算效率与可扩展性。