Hyperion——一种用于连续时间SLAM的快速、通用的符号化高斯置信度传播框架
Hyperion -- A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAM
AI总结：
针对连续时间SLAM计算量大、多为集中式优化的问题，研究提出了高速的SymForce基B样条与Z样条实现，以及名为Hyperion的分布式连续时间高斯置信度传播框架，在速度上实现显著提升并验证了方法有效性。
摘要：
连续时间同时定位与建图（CTSLAM）已成为融合异步多模态传感器套件的一种有前景的方法。与离散估计位姿的离散时间SLAM不同，CTSLAM采用连续时间运动参数化，便于集成卷帘相机、事件相机和惯性测量单元（IMU）等多种传感器。然而，CTSLAM方法的计算量仍然很大，且通常被建模为集中式非线性最小二乘（NLLS）优化问题。针对这些局限，我们不仅提出了基于SymForce[Martiros等人，RSS 2022]的最快B样条和Z样条实现，相较于Sommer等人[CVPR 2020]的方法实现了2.43倍到110.31倍的速度提升，还实现了一种名为Hyperion的新型连续时间高斯置信度传播（GBP）框架，面向多智能体间的分布式概率推理。我们在运动跟踪和定位场景中验证了该方法的有效性，并辅以实证消融研究。
英文摘要：
Continuous-Time Simultaneous Localization And Mapping (CTSLAM) has become a promising approach for fusing asynchronous and multi-modal sensor suites. Unlike discrete-time SLAM, which estimates poses discretely, CTSLAM uses continuous-time motion parametrizations, facilitating the integration of a variety of sensors such as rolling-shutter cameras, event cameras and Inertial Measurement Units (IMUs). However, CTSLAM approaches remain computationally demanding and are conventionally posed as centralized Non-Linear Least Squares (NLLS) optimizations. Targeting these limitations, we not only present the fastest SymForce-based [Martiros et al., RSS 2022] B- and Z-Spline implementations achieving speedups between 2.43x and 110.31x over Sommer et al. [CVPR 2020] but also implement a novel continuous-time Gaussian Belief Propagation (GBP) framework, coined Hyperion, which targets decentralized probabilistic inference across agents. We demonstrate the efficacy of our method in motion tracking and localization settings, complemented by empirical ablation studies.
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