Active perception network for non-myopic online exploration and visual surface coverage

TL;DR

This paper introduces the Active Perception Network, a hierarchical topological graph for efficient online exploration and visual coverage of unknown environments, improving coverage and exploration time.

Contribution

The paper presents a novel hierarchical perception roadmap (APN) with difference-awareness and frontier-guided view sampling, enabling non-myopic exploration of complex environments.

Findings

Significant reduction in exploration time compared to state-of-the-art methods

High computational efficiency scalable to large environments

Improved surface coverage and exploration completeness

Abstract

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.