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:(二) 在探索期间,根据直观机制更新了2D楼图的概率分布图,而改进的信念更新则提高了代理人勘探的有效性。 (三) 我们认识到,通过利用特定探测器的成功统计数据,物体探测器可能无法进入上述概率建模,我们认识到,通过利用特定探测器的成功统计数据,物体探测器可能无法进入上述概率建模。 我们的解决方案被称为POMP-BE-PD(基于Pomcp的探索与概率检测的信仰在线规划在线运动),没有受到监督,因为不需要进行任何培训课程。 (二) 在探索期间,根据直观机制更新了2D楼图的概率分布,同时利用改进的信念更新提高了该代理人勘探的实效。 (三) 我们采用积极愿景数据库基准,将所有环境的平均成功率提高至相当的35%,同时不使用任何竞合的路径。</s>