基于相机-声呐融合的水下垃圾主动测绘
Active Mapping of Underwater Litter Using Camera-Sonar Fusion
查看机构详情
- Technical University of Cluj-Napoca（克卢日-纳波卡技术大学）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
针对水下垃圾调查效率低的问题，提出相机与声呐融合的主动测绘框架，通过贝叶斯占用图与优化选择最佳视角，在模拟中比割草机路径更快发现目标，且双传感器优于单传感器。
中文摘要 AI 辅助
海洋垃圾对水下生态系统构成日益严重的威胁，推动了对能够在大范围内高效定位碎片的自主调查方法的需求。现有的调查方法通常遵循预定义路径或使用单一传感模态，通常是相机（在低能见度条件下图像质量受损）或声呐（通常噪声大且分辨率低）。我们提出了一种主动测绘框架，其中前视声呐和相机共同输入到共享的贝叶斯占用地图中，并在每一步求解一个优化问题以决定下一个最佳视角。候选视点通过一个双项效用函数进行评分，该函数通过体素熵平衡对不确定区域的探索与对可能目标的利用。每个传感器由从数据中确定的随距离和方位变化的检测概率表和误报概率表来表征。我们在一个逼真的水下模拟器中评估了该方法，证明主动测绘比割草机式覆盖模式更快地发现目标，并且双传感器方法比单独使用任一传感器效果更好。
英文摘要
Marine litter is a growing threat to the underwater ecosystem, driving demand for autonomous survey methods that can locate debris efficiently over large areas. Existing survey methods typically follow predefined paths or operate with a single sensing modality, typically a camera (with image quality suffering in poor-visibility conditions) or sonar (usually noisy and low-resolution). We present an active mapping framework in which a forward-looking sonar and a camera both feed into a shared Bayesian occupancy map, and an optimization problem is solved at each step to decide on the next best view. Candidate viewpoints are scored by a two-term utility that balances exploration of uncertain regions via voxel entropy against exploitation of likely objects. Each sensor is characterized by range- and bearing-dependent detection and false-alarm probability tables determined from data. We evaluate the approach in a realistic underwater simulator, demonstrating that active mapping finds objects faster than a lawnmower coverage pattern, and that the dual-sensor approach works better than using either of the individual sensors.
