Hyperion -- A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAM

Autor:	Hug, David, Alzugaray, Ignacio, Chli, Margarita
Rok vydání:	2024
Předmět:	Computer Science - Robotics Computer Science - Computer Vision and Pattern Recognition Computer Science - Symbolic Computation
Druh dokumentu:	Working Paper
Popis:	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. Comment: To be published in ECCV 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.07074 Zobrazit plný text záznamu View this record from Arxiv