LoGoPlanner: Localization Grounded Navigation Policy with Metric-aware Visual Geometry
Jiaqi Peng, Wenzhe Cai, Yuqiang Yang, Tai Wang, Yuan Shen, Jiangmiao Pang

TL;DR
LoGoPlanner is an end-to-end navigation system for mobile robots that uses visual geometry to improve localization accuracy, obstacle avoidance, and generalization across different environments and robot embodiments.
Contribution
It introduces a novel framework that finetunes visual geometry for absolute scale, reconstructs scene geometry from observations, and conditions policies on implicit geometry, enhancing robustness and accuracy.
Findings
Achieves over 27.3% improvement over baseline localization methods.
Demonstrates strong generalization across different robot types and environments.
Reduces cumulative error in trajectory planning.
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…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Robotics and Sensor-Based Localization · Multimodal Machine Learning Applications
