Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation
Xiaohan Lei, Min Wang, Wengang Zhou, Li Li, Houqiang Li

TL;DR
This paper introduces a novel Instance-aware Exploration-Verification-Exploitation framework for instance image goal navigation, enabling agents to better identify and reach specific objects in complex environments, outperforming previous methods.
Contribution
The paper proposes a new modular navigation framework that actively switches among exploration, verification, and exploitation to improve instance-level navigation performance.
Findings
Outperforms previous state-of-the-art on HM3D-SEM dataset
Achieves higher success rates with classical and robust segmentation models
Demonstrates effective instance identification during navigation
Abstract
As a new embodied vision task, Instance ImageGoal Navigation (IIN) aims to navigate to a specified object depicted by a goal image in an unexplored environment. The main challenge of this task lies in identifying the target object from different viewpoints while rejecting similar distractors. Existing ImageGoal Navigation methods usually adopt the simple Exploration-Exploitation framework and ignore the identification of specific instance during navigation. In this work, we propose to imitate the human behaviour of ``getting closer to confirm" when distinguishing objects from a distance. Specifically, we design a new modular navigation framework named Instance-aware Exploration-Verification-Exploitation (IEVE) for instance-level image goal navigation. Our method allows for active switching among the exploration, verification, and exploitation actions, thereby facilitating the…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Vision and Imaging · Robotic Path Planning Algorithms
