Efficient visual perception using mobile systems is crucial, particularly in unknown environments such as search and rescue operations, where swift and comprehensive perception of objects of interest is essential. In such real-world applications, objects of interest are often situated in complex environments, making the selection of the 'Next Best' view based solely on maximizing visibility gain suboptimal. Semantics, providing a higher-level interpretation of perception, should significantly contribute to the selection of the next viewpoint for various perception tasks. In this study, we formulate a novel information gain that integrates both visibility gain and semantic gain in a unified form to select the semantic-aware Next-Best-View. Additionally, we design an adaptive strategy with termination criterion to support a two-stage search-and-acquisition manoeuvre on multiple objects of interest aided by a multi-degree-of-freedoms (Multi-DoFs) mobile system. Several semantically relevant reconstruction metrics, including perspective directivity and region of interest (ROI)-to-full reconstruction volume ratio, are introduced to evaluate the performance of the proposed approach. Simulation experiments demonstrate the advantages of the proposed approach over existing methods, achieving improvements of up to 27.13% for the ROI-to-full reconstruction volume ratio and a 0.88234 average perspective directivity. Furthermore, the planned motion trajectory exhibits better perceiving coverage toward the target.

翻译：利用移动系统实现高效视觉感知至关重要，尤其是在未知环境（如搜索与救援任务）中，快速且全面地感知目标对象是关键。在此类实际应用中，目标对象往往处于复杂环境中，仅依据最大化可见性增益选择“下一最佳”视点可能并非最优。语义作为对感知过程的高层次解释，应能显著提升不同感知任务中下一视点的选择质量。本研究提出一种新型信息增益函数，将可见性增益与语义增益统一建模，用于选择语义感知的下一最佳视点。此外，我们设计了一种带有终止准则的自适应策略，以支持多自由度移动系统对多个目标对象进行两阶段搜索与获取操作。为评估所提方法性能，引入了若干与语义相关的重建指标，包括视角方向性及感兴趣区域（ROI）与完整重建体积比。仿真实验表明，所提方法相比现有方法具有优势：ROI与完整重建体积比提升最高达27.13%，平均视角方向性达0.88234。此外，规划的运动轨迹对目标感知覆盖更优。