Frontier Semantic Exploration for Visual Target Navigation
Bangguo Yu, Hamidreza Kasaei, Ming Cao

TL;DR
This paper introduces a frontier semantic policy framework for visual target navigation that improves exploration efficiency and success rates in unknown environments using deep reinforcement learning and semantic mapping.
Contribution
It proposes a novel framework combining semantic and frontier maps with deep RL to enhance exploration in large unknown scenes, outperforming existing methods.
Findings
Significantly higher success rate in navigation tasks.
More efficient exploration policy learned from frontiers.
Effective transferability to real-world scenarios.
Abstract
This work focuses on the problem of visual target navigation, which is very important for autonomous robots as it is closely related to high-level tasks. To find a special object in unknown environments, classical and learning-based approaches are fundamental components of navigation that have been investigated thoroughly in the past. However, due to the difficulty in the representation of complicated scenes and the learning of the navigation policy, previous methods are still not adequate, especially for large unknown scenes. Hence, we propose a novel framework for visual target navigation using the frontier semantic policy. In this proposed framework, the semantic map and the frontier map are built from the current observation of the environment. Using the features of the maps and object category, deep reinforcement learning enables to learn a frontier semantic policy which can be…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Robotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques
