Autonomous mobile robots (AMRs) equipped with high-quality cameras have revolutionized the field of inspections by providing efficient and cost-effective means of conducting surveys. The use of autonomous inspection is becoming more widespread in a variety of contexts, yet it is still challenging to acquire the best inspection information autonomously. In situations where objects may block a robot's view, it is necessary to use reasoning to determine the optimal points for collecting data. Although researchers have explored cloud-based applications to store inspection data, these applications may not operate optimally under network constraints, and parsing these datasets can be manually intensive. Instead, there is an emerging requirement for AMRs to autonomously capture the most informative views efficiently. To address this challenge, we present an autonomous Next-Best-View (NBV) framework that maximizes the inspection information while reducing the number of pictures needed during operations. The framework consists of a formalized evaluation metric using ray-tracing and Gaussian process interpolation to estimate information reward based on the current understanding of the partially-known environment. A derivative-free optimization (DFO) method is used to sample candidate views in the environment and identify the NBV point. The proposed approach's effectiveness is shown by comparing it with existing methods and further validated through simulations and experiments with various vehicles.

翻译：配备高质量相机的自主移动机器人通过提供高效且经济可行的调查手段，彻底改变了巡检领域。自主巡检的应用正日益广泛，但如何自主获取最佳的巡检信息仍是一大挑战。当物体可能阻挡机器人视野时，必须通过推理来确定数据采集的最优位置。尽管研究者已探索基于云端应用存储巡检数据，但这些应用在网络约束下可能难以最优运行，且解析数据集需要大量人工操作。因此，亟需自主移动机器人能够高效捕捉最具信息量视角的能力。为应对这一挑战，我们提出一种自主次佳视角（NBV）框架，该框架能在最大化巡检信息的同时，减少操作过程中所需的图像数量。该框架包含一个形式化的评估指标，利用光线追踪和高斯过程插值方法，基于当前对部分已知环境的理解来估计信息收益。采用无导数优化（DFO）方法对环境中的候选视角进行采样，并识别出次佳视角点。通过与现有方法的对比，以及基于多种载具的仿真与实验验证，证明了所提方法的有效性。