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).
翻译:我们引入了PRISM, 这是一种在代理人运动和视觉感知的概率基因模型中实时过滤的方法。以前的方法要么缺乏地图和代理状态的不确定性估计,要么没有实时运行,没有密集的现场代表,或者没有模拟代理动态。我们的解决方案协调了所有这些方面。我们从一个预先定义的状态空间模型开始,该模型将不同的成份和6-DoF动态结合起来。该模型中的概率推论相当于同时进行本地化和绘图(SLAM),并且是难以解决的。我们使用一系列对巴伊西亚推断的近似值来得出概率性地图和状态估计。我们利用完善的方法和封闭式更新,保持准确性和使实时能力成为可能。拟议解决方案运行在10Hz实时运行,与中小型室内环境的SLAM状态相似,使用高速的UAV和手持照相剂(Blackbir、EuRoC和TUM-RGBD)。