MAC-I2: learned metrics-aware covariance for robust visual-inertial fusion
zhenjun_zhao · x · 2026-09-11
A robotics paper from CMU (Wenshan Wang, Sebastian Scherer et al.) presents MAC-I², which replaces predefined uncertainties in visual-inertial fusion with learned metrics-aware covariances that faithfully reflect actual measurement noise, letting vision and IMU compete on their own merits under illumination changes, dynamic objects and textureless scenes. Visually, learned feature-matching uncertainties are propagated into pose covariances; on the inertial side, an IMU model with a learnable initial covariance plus fine-tuning on a held-out subset handles early-stage error accumulation, supporting robust initialization and calibration.
More from Embodied
- FLASH: GPU-native deformable sim learns cloth folding in minutes on one RTX 5090 — ChongZzZhang · 2026-10-26
- Luke Wroblewski spots it: iPhone Duo's corner radius mirrors the original iPhone — LukeW · 2026-10-26
- Humanoids shattered forecasts: half-marathon record in 2026, 100m faster than Bolt — ben_j_todd · 2026-10-26
- Humanoid walking is continuous correction — watch recovery, not locomotion, in demos — Olivier__OG · 2026-10-26
- CircuitHub unveils The Grid, a factory-scale robot making PCBs in the US twice as fast as Shenzhen — garrytan · 2026-10-26
- Shanghai AI Lab-led team releases 100-hour egocentric wearable dataset for humanoid training — ChuckDBrooks · 2026-10-26