SE(2) NavMesh adds yaw-dependent traversability layers to polygonal navigation meshes, paired with ASA hierarchical pathfinding and incremental point-cloud updates, claiming over 50% more traversable area than standard navmeshes.
Elevation mapping for locomotion and navigation using gpu
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.RO 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
Foot-mounted proximity sensors provide pre-contact feedback that, when integrated into RL, improves quadruped traversal robustness on discrete terrain with reliable sim-to-real transfer.
Introduces an incremental reachable graph and structural priors for multi-floor ground robot exploration, showing improved efficiency in simulation and real-time onboard performance.
Integrating foot position maps into heightmaps and adding a locomotion-stability reward in an attention-based RL framework improves quadrupedal success rates on both trained and out-of-domain complex terrains.
citing papers explorer
-
SE(2) Navigation Mesh
SE(2) NavMesh adds yaw-dependent traversability layers to polygonal navigation meshes, paired with ASA hierarchical pathfinding and incremental point-cloud updates, claiming over 50% more traversable area than standard navmeshes.
-
Learning Locomotion on Discrete Terrain via Minimal Proximity Sensing
Foot-mounted proximity sensors provide pre-contact feedback that, when integrated into RL, improves quadruped traversal robustness on discrete terrain with reliable sim-to-real transfer.
-
Multi-Floor Exploration for Ground Robots via an Incremental Reachable Graph and Structural Priors
Introduces an incremental reachable graph and structural priors for multi-floor ground robot exploration, showing improved efficiency in simulation and real-time onboard performance.
-
Learning Locomotion on Complex Terrain for Quadrupedal Robots with Foot Position Maps and Stability Rewards
Integrating foot position maps into heightmaps and adding a locomotion-stability reward in an attention-based RL framework improves quadrupedal success rates on both trained and out-of-domain complex terrains.