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Published as a conference paper at NeurIPS 2026

LiteNav: Lightweight Map-free Outdoor Visual Navigation

Mingxu Zhu, Chang Liu, Xiao Zhao, Zheyuan Zhang, Linna Song, Qingliang Luo, liming zhang, Xiangyu Zhen, Chufan Guo, Kuifeng Su

Abstract

Outdoor robot navigation in dynamic, unknown environments remains a formidable challenge, necessitating robust traversability assessment and collision-free planning. While traditional map-based approaches struggle with adaptability to novel scenes, existing map-free methods frequently incur high computational costs and depend heavily on LiDAR data. In response, we propose LiteNav, a vision-only for exteroceptive sensing and strictly map-free outdoor navigation system. Relying solely on a single off-the-shelf RGB camera and GPS for goal specification, LiteNav estimates traversability, encodes navigation goals, and generates candidate trajectories via a diffusion-based joint encoding model. These trajectories are then refined by a goal-oriented planner to ensure correctness, efficiency, and safety. Remarkably, LiteNav achieves performance comparable to LiDAR-equipped methods without ever constructing or referencing explicit maps. Deployed on a low-power NVIDIA Jetson Xavier NX, it operates in real time, consuming only one-fifth of the computational resources required by prior approaches. Experiments demonstrate that LiteNav achieves state-of-the-art results on the GND dataset and outperforms existing methods in real-world scenarios. In summary, LiteNav is a map-free outdoor navigation system that operates using monocular RGB perception and GPS goal cues on an embedded platform, demonstrating strong potential for scalable deployment.

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