Navigating efficiently to an object in an unexplored environment is a critical skill for general-purpose intelligent robots. Recent approaches to this object goal navigation problem have embraced a modular strategy, integrating classical exploration algorithms-notably frontier exploration-with a learned semantic mapping/exploration module. This paper introduces a novel informative path planning and 3D object probability mapping approach. The mapping module computes the probability of the object of interest through semantic segmentation and a Bayes filter. Additionally, it stores probabilities for common objects, which semantically guides the exploration based on common sense priors from a large language model. The planner terminates when the current viewpoint captures enough voxels identified with high confidence as the object of interest. Although our planner follows a zero-shot approach, it achieves state-of-the-art performance as measured by the Success weighted by Path Length (SPL) and Soft SPL in the Habitat ObjectNav Challenge 2023, outperforming other works by more than 20%. Furthermore, we validate its effectiveness on real robots. Project webpage: https://ippon-paper.github.io/

翻译：在未知环境中高效导航至目标物体是通用智能机器人的关键能力。针对目标物体导航问题，近期研究多采用模块化策略，将经典探索算法（尤其是前沿探索）与学习的语义建图/探索模块相结合。本文提出了一种新颖的信息化路径规划与三维物体概率建图方法。建图模块通过语义分割与贝叶斯滤波器计算目标物体的存在概率，同时存储常见物体的概率分布，并基于大语言模型提供的常识先验进行语义引导的探索。当当前视点捕获到足够多被高置信度识别为目标物体的体素时，规划器终止搜索。尽管我们的规划器采用零样本方法，但在Habitat 2023物体导航挑战赛中，以路径长度加权成功率（SPL）和软SPL指标衡量，其性能达到最先进水平，较其他方法提升超过20%。此外，我们在真实机器人平台上验证了该方法的有效性。项目网页：https://ippon-paper.github.io/