论文标题
棱镜：空间世界模型中的概率实时推断
PRISM: Probabilistic Real-Time Inference in Spatial World Models
论文作者
论文摘要
我们介绍了Prism，这是一种在代理运动和视觉感知的概率生成模型中实时过滤的方法。先前的方法要么缺乏对地图和代理状态的不确定性估计，要么实时运行，没有密集的场景表示，或者不模拟代理动力学。我们的解决方案调和了所有这些方面。我们从结合了可区分渲染和6-DOF动力学的预定义状态空间模型开始。该模型中的概率推论等于同时定位和映射（SLAM），并且很难。我们对贝叶斯推断使用一系列近似值来得出概率图和状态估计。我们利用了公认的方法和封闭式更新，可以保留准确性并实现实时功能。所提出的解决方案在实时10Hz时运行，与中小型室内环境中的最先进的大满贯同样准确，具有高速无人机和手持式摄像头（Blackbird，Euroc和Tum-RGBD）。
We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception. Previous approaches either lack uncertainty estimates for the map and agent state, do not run in real-time, do not have a dense scene representation or do not model agent dynamics. Our solution reconciles all of these aspects. We start from a predefined state-space model which combines differentiable rendering and 6-DoF dynamics. Probabilistic inference in this model amounts to simultaneous localisation and mapping (SLAM) and is intractable. We use a series of approximations to Bayesian inference to arrive at probabilistic map and state estimates. We take advantage of well-established methods and closed-form updates, preserving accuracy and enabling real-time capability. The proposed solution runs at 10Hz real-time and is similarly accurate to state-of-the-art SLAM in small to medium-sized indoor environments, with high-speed UAV and handheld camera agents (Blackbird, EuRoC and TUM-RGBD).
