RootSLAM：面向视觉-惯性SLAM的平方根光束平差法
RootSLAM: Square Root Bundle Adjustment for Visual-Inertial SLAM
AI总结：
本研究提出基于Basalt的RootSLAM视觉-惯性SLAM系统，通过适配平方根光束平差法实现，精度与顶尖水平相当且速度快达20倍，将开源相关组件以助力资源受限环境的感知系统部署。
摘要：
高效感知是机器人技术、混合现实、自动驾驶和无人机应用所针对的小型低功耗设备的前提，这些设备均依赖同步定位与建图（SLAM）来估计自身在环境中的运动。基于学习的SLAM系统达到了顶尖精度，但对计算资源要求高；而传统的基于几何的方法则保持着强大的效率优势，且精度几乎没有损失。本研究提出RootSLAM，这是一个基于Basalt构建的、面向多相机设置的全功能视觉-惯性SLAM系统。RootSLAM达到了与顶尖水平相当的精度，同时运行速度快达20倍，便于在资源受限环境中部署感知系统。我们将来自运动恢复结构的平方根光束平差法公式，通过利用系统位姿图的新型重构，适配到多相机视觉-惯性SLAM场景中，使全局优化器能以单精度浮点数运行，从而将性能提升一倍。我们针对顶尖水平开展了全面基准测试，并对我们的贡献进行了 ablation 研究。我们的新实现，包括其地图管理和回环检测模块、位姿图优化器以及全局建图组件，将开源。
英文摘要：
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, facilitating the deployment of perception systems in resource constrained environments. 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. We provide a thorough benchmark against the state of the art together with an ablation study of our contributions. Our new implementation, including its map management and loop detection module, pose-graph optimizer, and global mapping components, will be open-sourced.
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