# Khronos: A Unified Approach for Spatio-Temporal Metric-Semantic SLAM in Dynamic Environments

arXiv: https://arxiv.org/abs/2402.13817 · HTML: https://mlsanity.com/paper/2402.13817/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2024.

## Built on by (most cited first, 30 of 36)

- [DynaMem: Online Dynamic Spatio-Semantic Memory for Open World Mobile Manipulation](https://mlsanity.com/paper/2411.04999/) (2024)
- [Describe Anything Anywhere At Any Moment](https://mlsanity.com/paper/2512.00565/) (2025)
- [OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging](https://mlsanity.com/paper/2503.01309/) (2025)
- [Lost & Found: Tracking Changes from Egocentric Observations in 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2411.19162/) (2024)
- [FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment](https://mlsanity.com/paper/2504.08603/) (2025)
- [DualMap: Online Open-Vocabulary Semantic Mapping for Natural Language Navigation in Dynamic Changing Scenes](https://mlsanity.com/paper/2506.01950/) (2025)
- [Pandora: Articulated 3D Scene Graphs from Egocentric Vision](https://mlsanity.com/paper/2603.28732/) (2026)
- [Toward General Object-level Mapping from Sparse Views with 3D Diffusion Priors](https://mlsanity.com/paper/2410.05514/) (2024)
- [Bayesian Fields: Task-driven Open-Set Semantic Gaussian Splatting](https://mlsanity.com/paper/2503.05949/) (2025)
- [SceneDiff: A Benchmark and Method for Multiview Object Change Detection](https://mlsanity.com/paper/2512.16908/) (2025)
- [3D Scene Graphs: Open Challenges and Future Directions](https://mlsanity.com/paper/2606.19383/) (2026)
- [Gaussian Mapping for Evolving Scenes](https://mlsanity.com/paper/2506.06909/) (2025)
- [Changes in Real Time: Online Scene Change Detection with Multi-View Fusion](https://mlsanity.com/paper/2511.12370/) (2025)
- [SceneLinker: Compositional 3D Scene Generation via Semantic Scene Graph from RGB Sequences](https://mlsanity.com/paper/2602.02974/) (2026)
- [Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees](https://mlsanity.com/paper/2606.08277/) (2026)
- [BYE: Build Your Encoder with One Sequence of Exploration Data for Long-Term Dynamic Scene Understanding](https://mlsanity.com/paper/2412.02449/) (2024)
- [Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments](https://mlsanity.com/paper/2507.18808/) (2025)
- [M3DMap: Object-aware Multimodal 3D Mapping for Dynamic Environments](https://mlsanity.com/paper/2508.17044/) (2025)
- [DISC: Dense Integrated Semantic Context for Large-Scale Open-Set Semantic Mapping](https://mlsanity.com/paper/2603.03935/) (2026)
- [Efficient Feature-Free Initialization for Monocular Visual-Inertial Systems Using a Feed-Forward 3D Model](https://mlsanity.com/paper/2605.17327/) (2026)
- [LT-Mem: Volatility-Aware Spatio-Temporal Memory for Lifelong Scene Understanding](https://mlsanity.com/paper/2608.19059/) (2026)
- [DSG: Dynamic 3D Scene Graph Construction for Embodied Agents in Changing Indoor Environments](https://mlsanity.com/paper/2609.00619/) (2026)
- [Build Once, Monitor Continuously: Persistent Semantic Mapping via Autonomous Exploration and Open-Vocabulary Object Updates](https://mlsanity.com/paper/2409.15493/) (2024)
- [STaR: Scalable Task-Conditioned Retrieval for Long-Horizon Multimodal Robot Memory](https://mlsanity.com/paper/2602.09255/) (2026)
- [Long-Term Multi-Session 3D Reconstruction Under Substantial Appearance Change](https://mlsanity.com/paper/2602.20584/) (2026)
- [Con-DSO: Learning Short-Horizon Consistency Priors for RGB-D Direct Sparse Odometry](https://mlsanity.com/paper/2605.27952/) (2026)
- [Belief Consistency Between Foundation-Model Evidence and Geometric Perception in Persistent Robotic Maps](https://mlsanity.com/paper/2606.00318/) (2026)
- [PreSIST: Vision-Language-Informed Object Persistence Prediction in Open-World Scenes](https://mlsanity.com/paper/2607.04057/) (2026)
- [Hydra++: Real-Time Hierarchical 3D Scene Graph Construction With Object-Level Shape Estimation](https://mlsanity.com/paper/2607.09455/) (2026)
- [Prior-SG: Task and Prior Driven Region Segmentation for Scene Graphs in Arbitrarily-Structured Environments](https://mlsanity.com/paper/2608.06170/) (2026)

## Abstract (arXiv)

Perceiving and understanding highly dynamic and changing environments is a crucial capability for robot autonomy. While large strides have been made towards developing dynamic SLAM approaches that estimate the robot pose accurately, a lesser emphasis has been put on the construction of dense spatio-temporal representations of the robot environment. A detailed understanding of the scene and its evolution through time is crucial for long-term robot autonomy and essential to tasks that require long-term reasoning, such as operating effectively in environments shared with humans and other agents and thus are subject to short and long-term dynamics. To address this challenge, this work defines the Spatio-temporal Metric-semantic SLAM (SMS) problem, and presents a framework to factorize and solve it efficiently. We show that the proposed factorization suggests a natural organization of a spatio-temporal perception system, where a fast process tracks short-term dynamics in an active temporal window, while a slower process reasons over long-term changes in the environment using a factor graph formulation. We provide an efficient implementation of the proposed spatio-temporal perception approach, that we call Khronos, and show that it unifies exiting interpretations of short-term and long-term dynamics and is able to construct a dense spatio-temporal map in real-time. We provide simulated and real results, showing that the spatio-temporal maps built by Khronos are an accurate reflection of a 3D scene over time and that Khronos outperforms baselines across multiple metrics. We further validate our approach on two heterogeneous robots in challenging, large-scale real-world environments.