Abstract
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.
Abstract (translated)
高效的移动设备视觉感知对于移动系统来说至关重要，尤其是在未知环境中，如搜索和救援行动，快速全面地感知感兴趣的对象是至关重要的。在这类现实应用中，感兴趣的对象通常位于复杂的环境中，因此仅基于最大化可见性增益来选择下一个视角是不够的。语义信息，提供对感知的高级解释，应该对各种感知任务的下一个视角选择产生显著影响。在本研究中，我们提出了一种新颖的信息增益形式，将可见性增益和语义增益统一在一起，以选择具有语义意识的下一个最好的视角。此外，我们设计了一个支持多自由度（Multi-DoFs）移动系统两个阶段搜索与获取动作的适应策略。为了评估所提出方法的表现，我们引入了一些语义相关的重构指标，包括视点定向和感兴趣区域（ROI）到完整重建体积比。仿真实验证明，与现有方法相比，所提出的方法具有明显的优势，ROI-到完整重建体积比的提升幅度达到27.13%，平均视点定向为0.88234。此外，计划运动轨迹对目标区域的感知覆盖面更好。
URL
https://arxiv.org/abs/2404.16507
