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arXiv · 2410.19697

IPPON: Common Sense Guided Informative Path Planning for Object Goal Navigation

Kaixian Qu·Jie Tan·Tingnan Zhang·Fei Xia·Cesar Cadena·Marco Hutter
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Abstract

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/

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Kaixian Qu, Jie Tan, Tingnan Zhang, Fei Xia, Cesar Cadena, Marco Hutter. 2024-10-25. IPPON: Common Sense Guided Informative Path Planning for Object Goal Navigation. https://arxiv.org/abs/2410.19697

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