Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes
Miguel Saavedra-Ruiz, Charlie Gauthier, Kumaraditya Gupta, Shima Shahfar, Kirsty Ellis, Steven Parkison, Liam Paull

TL;DR
This paper introduces PredictiveGraphs, a method combining Bayesian filtering with spatio-semantic scene graphs to enable robots to reason about and predict environment changes over time.
Contribution
The work presents a novel framework integrating Perpetua* Bayesian filters within 3D scene graphs for tempo-spatio-semantic reasoning in semi-static environments.
Findings
Outperforms baselines in predicting future environment states
Effective in both simulation and real-world dynamic navigation tasks
Handles environment changes occurring bi-hourly over three weeks
Abstract
We have seen tremendous recent progress in our ability to build "spatio-semantic" representations that enable robots to perform complex reasoning across geometry and semantics. However, the vast majority of these methods lack any ability to perform reasoning across time. This is a desirable property in situations where a robot repeatedly observes an environment where instances may change in between observations, but in a structured way. Consider as an example a home environment where the location of a mug typically moves from the cupboard to a countertop to the sink and then back to the cupboard on a daily basis. We should be able to learn this cyclic behavior and use it to predict the state of the mug in the future. In this work, we propose a method that is able to perform this type of tempo-spatio-semantic reasoning. Underpinning the method is a filter, Perpetua, that performs…
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