LoGoPlanner: Localization Grounded Navigation Policy with Metric-aware Visual Geometry
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/{project page}.
694a13d0335742716e9322a0
Jiaqi Peng
claimed_verified
2025-12-25T20:48:20.962Z
642a7fd8ecec03b44645987c
https://cdn-avatars.huggingface.co/v1/production/uploads/642a7fd8ecec03b44645987c/5jFttXA8TtfugX7oR_8ah.jpeg
Jiaqi Peng
Steinate
user
Steinate
694a13d0335742716e9322a1
Wenzhe Cai
694a13d0335742716e9322a2
Yuqiang Yang
694a13d0335742716e9322a3
Tai Wang
694a13d0335742716e9322a4
Yuan Shen
694a13d0335742716e9322a5
Jiangmiao Pang
LoGoPlanner: Localization Grounded Navigation Policy with Metric-aware Visual Geometry
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/{project page}.
LoGoPlanner is an end-to-end navigation framework that improves trajectory planning in unstructured environments by integrating localization, scene geometry reconstruction, and policy conditioning.
trajectory planning unstructured environments end-to-end learning localization scene geometry reconstruction policy conditioning long-horizon visual-geometry backbone implicit state estimation obstacle avoidance metric-aware geometry memory