SF-ODNav: Successor Feature Framework for Map-Less Target-Driven Outdoor Visual Navigation

Abstract

Traditional deep reinforcement learning-based visual navigation techniques face challenges in dynamic and unstructured outdoor environments, particularly in the absence of high-resolution maps and GPS signals. This paper presents a deep reinforcement learning-based approach for target-driven visual navigation without explicit localization and mapping in outdoor settings, using the successor feature (SF) framework to enhance the model's transfer learning. This design enables effective knowledge transfer across tasks, allowing the model to adapt to novel environments with zero-shot or few-shot fine-tuning. To facilitate training and evaluation, we design grid-world environments constructed from real-world outdoor images, providing realistic yet controlled conditions for developing and testing deep reinforcement learning-based navigation. Experimental results demonstrate that our method can adapt effectively in outdoor environments, both within the same domain and across different domains. Moreover, despite being trained in a discrete grid-world setting, the model is successfully deployed in real time within the same area, maintaining robust performance and highlighting its strong transferability to continuous, real-world conditions.