RootSLAM: Square Root Bundle Adjustment for Visual-Inertial SLAM

Key points

• 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.1
• 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.1
• RootSLAM achieves accuracy competitive with the state of the art while running up to 20 times faster, facilitating the deployment of perception systems in resource constrained environments.1
• We adapt the square-root bundle adjustment formulation from structure from motion into the multi-camera visual-inertial SLAM setting through a novel reformulation that leverages the system's pose-graph, letting the global optimizer run in single-precision floating point, thus doubling its performance.1

Sources (1)

• [1]RootSLAM: Square Root Bundle Adjustment for Visual-Inertial SLAM
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 08:15 PM

  In this work we present RootSLAM, a fully-featured visual-inertial SLAM system for multi-camera setups built on Basalt.

  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.

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