We propose a solution for Active Visual Search of objects in an environment, whose 2D floor map is the only known information. Our solution has three key features that make it more plausible and robust to detector failures compared to state-of-the-art methods: (i) it is unsupervised as it does not need any training sessions. (ii) During the exploration, a probability distribution on the 2D floor map is updated according to an intuitive mechanism, while an improved belief update increases the effectiveness of the agent's exploration. (iii) We incorporate the awareness that an object detector may fail into the aforementioned probability modelling by exploiting the success statistics of a specific detector. Our solution is dubbed POMP-BE-PD (Pomcp-based Online Motion Planning with Belief by Exploration and Probabilistic Detection). It uses the current pose of an agent and an RGB-D observation to learn an optimal search policy, exploiting a POMDP solved by a Monte-Carlo planning approach. On the Active Vision Database benchmark, we increase the average success rate over all the environments by a significant 35% while decreasing the average path length by 4% with respect to competing methods. Thus, our results are state-of-the-art, even without using any training procedure.

翻译：我们提出了一种在仅已知二维楼层地图的环境中实现主动视觉搜索物体问题的解决方案。与现有方法相比，该方案具有三项关键特性，使其对探测器故障更具鲁棒性与可行性：(i) 无需任何训练过程，具备无监督特性；(ii) 探索过程中，基于直观机制更新二维楼层地图上的概率分布，同时改进的置信度更新机制提升智能体探索效率；(iii) 通过将特定探测器的成功统计信息融入上述概率模型，将目标探测器可能失效的认知纳入考量。该方案被命名为POMP-BE-PD（基于信念探索与概率检测的Pomcp在线运动规划），通过蒙特卡洛规划方法求解部分可观测马尔可夫决策过程（POMDP），利用智能体当前位姿与RGB-D观测学习最优搜索策略。在主动视觉数据库基准测试中，与竞争方法相比，该方法在所有环境中的平均成功率显著提升35%，同时平均路径长度缩短4%。即使未采用任何训练流程，我们的结果仍达到当前最优水平。