CVROOct 11, 2023

FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation

arXiv:2310.07473v136 citationsh-index: 19
AI Analysis

This work improves autonomous navigation systems by enabling more accurate and efficient image-specified goal reasoning, though it is incremental as it builds on existing navigation policy frameworks.

The paper tackles the challenge of image-goal navigation by addressing limitations in existing methods that miss detailed goal information and fail to focus on goal-relevant regions in observations, resulting in a method that surpasses the state-of-the-art success rate by 8% on the Gibson dataset with only 1/50 the model size.

Learning to navigate to an image-specified goal is an important but challenging task for autonomous systems. The agent is required to reason the goal location from where a picture is shot. Existing methods try to solve this problem by learning a navigation policy, which captures semantic features of the goal image and observation image independently and lastly fuses them for predicting a sequence of navigation actions. However, these methods suffer from two major limitations. 1) They may miss detailed information in the goal image, and thus fail to reason the goal location. 2) More critically, it is hard to focus on the goal-relevant regions in the observation image, because they attempt to understand observation without goal conditioning. In this paper, we aim to overcome these limitations by designing a Fine-grained Goal Prompting (FGPrompt) method for image-goal navigation. In particular, we leverage fine-grained and high-resolution feature maps in the goal image as prompts to perform conditioned embedding, which preserves detailed information in the goal image and guides the observation encoder to pay attention to goal-relevant regions. Compared with existing methods on the image-goal navigation benchmark, our method brings significant performance improvement on 3 benchmark datasets (i.e., Gibson, MP3D, and HM3D). Especially on Gibson, we surpass the state-of-the-art success rate by 8% with only 1/50 model size. Project page: https://xinyusun.github.io/fgprompt-pages

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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