LoGoPlanner: Localization Grounded Navigation Policy with Metric-aware Visual Geometry

• URL: http://arxiv.org/abs/2512.19629v2
• Date: Tue, 23 Dec 2025 05:37:16 GMT
• Title: LoGoPlanner: Localization Grounded Navigation Policy with Metric-aware Visual Geometry
• Authors: Jiaqi Peng, Wenzhe Cai, Yuqiang Yang, Tai Wang, Yuan Shen, Jiangmiao Pang,
• Abstract summary: Trajectory planning in unstructured environments is a fundamental and challenging capability for mobile robots.<n>We introduce LoGoPlanner, a localization-grounded, end-to-end navigation framework.<n>We evaluate LoGoPlanner in both simulation and real-world settings, where its fully end-to-end design reduces cumulative error.
• Score: 41.054069737969876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory planning in unstructured environments is a fundamental and challenging capability for mobile robots. Traditional modular pipelines suffer from latency and cascading errors across perception, localization, mapping, and planning modules. Recent end-to-end learning methods map raw visual observations directly to control signals or trajectories, promising greater performance and efficiency in open-world settings. However, most prior end-to-end approaches still rely on separate localization modules that depend on accurate sensor extrinsic calibration for self-state estimation, thereby limiting generalization across embodiments and environments. We introduce LoGoPlanner, a localization-grounded, end-to-end navigation framework that addresses these limitations by: (1) finetuning a long-horizon visual-geometry backbone to ground predictions with absolute metric scale, thereby providing implicit state estimation for accurate localization; (2) reconstructing surrounding scene geometry from historical observations to supply dense, fine-grained environmental awareness for reliable obstacle avoidance; and (3) conditioning the policy on implicit geometry bootstrapped by the aforementioned auxiliary tasks, thereby reducing error propagation. We evaluate LoGoPlanner in both simulation and real-world settings, where its fully end-to-end design reduces cumulative error while metric-aware geometry memory enhances planning consistency and obstacle avoidance, leading to more than a 27.3\% improvement over oracle-localization baselines and strong generalization across embodiments and environments. The code and models have been made publicly available on the https://steinate.github.io/logoplanner.github.io.

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