Rethinking Learned Occupancy in Autonomous Active Mapping with Observation-Gated Filtering

TL;DR

The paper identifies a critical flaw in active 3D mapping where learned occupancy predictions bias both sensing decisions and motion planning, leading to suboptimal coverage. It proposes an observation-gated filter that suppresses unreliable predictions until sufficient sensor evidence is gathered, improving performance in challenging scenarios without retraining. The method enhances robustness in autonomous mapping under intermittent communication.

Why It Matters

This work addresses a fundamental issue in space robotics and autonomous exploration: inaccurate geometric priors can mislead both where a robot looks and where it moves. By decoupling prediction reliability from planning, the approach enables safer, more efficient mapping in environments with limited or delayed sensor feedback, crucial for planetary missions.

Key Findings

• Using ground-truth occupancy leads to faster coverage convergence but only marginally higher final coverage compared to learned occupancy.
• Learned occupancy with no filtering causes planning errors due to unsupported predictions, degrading overall mapping performance.
• The observation-gated filter improves performance in failure-prone scenarios by suppressing predictions until multiple frustum observations confirm them, without requiring retraining or ground truth.