PRISM: Probabilistic Real-Time Inference in Spatial World Models

• URL: http://arxiv.org/abs/2212.02988v1
• Date: Tue, 6 Dec 2022 13:59:06 GMT
• Title: PRISM: Probabilistic Real-Time Inference in Spatial World Models
• Authors: Atanas Mirchev, Baris Kayalibay, Ahmed Agha, Patrick van der Smagt, Daniel Cremers, Justin Bayer
• Abstract summary: PRISM is a method for real-time filtering in a probabilistic generative model of agent motion and visual perception. 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.
• Score: 52.878769723544615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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