SE(2) Navigation Mesh
This paper introduces the SE(2) Navigation Mesh (SE(2) NavMesh), a polygonal representation that encodes yaw-dependent traversability for ground robots in complex multi-level environments. It uses footprint masks for traversability evaluation, builds a graph over yaw-specific layers with explicit translational and rotational connectivity, and proposes an A*-String Pulling-A* (ASA) path planning strategy. Simulations show over 50% more traversable area captured than classic NavMeshes, and real-world experiments on a physical robot validate real-time online generation and successful navigation.
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[Submitted on 1 Jul 2026]
Title:SE(2) Navigation Mesh
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Abstract:Global navigation for ground robots in complex multi-level environments requires representations that accurately capture traversable regions while enabling efficient path planning. Current approaches present key limitations: Point clouds and volumetric occupancy maps lack explicit surface structure for traversability estimation, whereas direct pathfinding on dense triangle meshes is computationally prohibitive. Navigation meshes mitigate these challenges through polygonal abstraction of the underlying mesh, but assume yaw-invariant traversability, rendering them unsuitable for non-circular robots in constrained spaces. We propose SE(2) Navigation Mesh (SE(2) NavMesh), a polygonal representation of traversable regions that encodes yaw-dependent traversability. Our method evaluates traversability using footprint masks and constructs a graph over yaw-specific layers with explicit translational and rotational connectivity. Grounded in this representation, we develop an A*-String Pulling-A* (ASA) pathfinding strategy that hierarchically optimizes robot position and heading. We also present an online method that incrementally updates the SE(2) NavMesh from streaming point clouds during concurrent geometry reconstruction. In simulation, the SE(2) NavMesh captures over 50% more traversable area than classical NavMeshes, and the SE(2) NavMesh + ASA pipeline consistently outperforms sampling-based baselines in constrained environments. Extensive real-world experiments on a physical robot validate real-time online generation and successful navigation across multiple environments.
Comments: Project page: this https URL
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.01454 [cs.RO]
(or arXiv:2607.01454v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.01454
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kaixian Qu [view email] [v1] Wed, 1 Jul 2026 20:21:02 UTC (14,648 KB)
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