EchoNav: Evidential BEV Completion from Historical Observations for Visual Navigation
EchoNav: Evidential BEV Completion from Historical Observations for Visual Navigation
This paper introduces EchoNav, a framework that combines temporal aggregation and occupancy completion in an evidential bird's-eye-view (BEV) representation to enable visual navigation in cluttered environments. The system adapts frozen VGGT-Omega features using historical residual proposals with cellwise weights and a learned no-update option, producing local maps without depth or camera-pose inputs. Trained on simulated RGB sequences with visibility-masked occupancy annotations, EchoNav achieves 86.3% success in simulated two-goal navigation (outperforming the strongest baseline by 32.2 percentage points) and 73.3% success across 60 trials on a wheeled robot in 20 scenes, compared to 33.3% for the baseline. Additional demonstrations were conducted on quadruped and humanoid robots.
The study demonstrates a framework combining temporal aggregation and evidential BEV completion for visual navigation, achieving 86.3% success in simulation and 73.3% across 60 wheeled-robot trials, with additional demonstrations on quadruped and humanoid robots.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source S4
Reported numbers
number of scenes for wheeled robot
20
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GB sequences paired with complete and visibility-masked occupancy annotations. Our experiments evaluate navigation in simulation and on a wheeled robot, with additional demonstrations on quadruped and humanoid robots. In simulated sequential two-goal navigation, our system achieves 86.3% success, outperforming the strongest evaluated baseline by 32.2 percentage points. Across 60 wheeled-robot trials in 20 scenes, it
Open source S4
- paper: not_reported
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GB sequences paired with complete and visibility-masked occupancy annotations. Our experiments evaluate navigation in simulation and on a wheeled robot, with additional demonstrations on quadruped and humanoid robots. In simulated sequential two-goal navigation, our system achieves 86.3% success, outperforming the strongest evaluated baseline by 32.2 percentage points. Across 60 wheeled-robot trials in 20 scenes, it
Open source S4
achieves 73.3% success, compared with 33.3% for the strongest evaluated baseline.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org