RootSLAM: Square Root Bundle Adjustment for Visual-Inertial SLAM
Efficient perception is a prerequisite for the small, low-power devices targeted by robotics, mixed reality, autonomous driving, and drone applications, all of which rely on Simultaneous Localization and Mapping (SLAM) to estimate their motion within their surroundings. Learning-based SLAM systems achieve state-of-the-art accuracy but are demanding in compute, whereas classical geometry-based methods retain a strong efficiency advantage with little to no loss in accuracy. In this work we present RootSLAM, a fully-featured visual-inertial SLAM system for multi-camera setups built on Basalt. RootSLAM achieves accuracy competitive with the state of the art while running up to 20 times faster...
| Authors | Tomás Santucci, Mateo de Mayo, Daniel Cremers et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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