# Lifelong LERF: Local 3D Semantic Inventory Monitoring Using FogROS2

arXiv: https://arxiv.org/abs/2403.10494 · HTML: https://mlsanity.com/paper/2403.10494/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2024.

## Built on by (most cited first, 5 of 5)

- [Neural Fields in Robotics: A Survey](https://mlsanity.com/paper/2410.20220/) (2024)
- [Gaussian Mapping for Evolving Scenes](https://mlsanity.com/paper/2506.06909/) (2025)
- [FogROS2-FT: Fault Tolerant Cloud Robotics](https://mlsanity.com/paper/2412.05408/) (2024)
- [M3DMap: Object-aware Multimodal 3D Mapping for Dynamic Environments](https://mlsanity.com/paper/2508.17044/) (2025)
- [Monocular 3D Object Position Estimation with VLMs for Human-Robot Interaction](https://mlsanity.com/paper/2603.01224/) (2026)

## Abstract (arXiv)

Inventory monitoring in homes, factories, and retail stores relies on maintaining data despite objects being swapped, added, removed, or moved. We introduce Lifelong LERF, a method that allows a mobile robot with minimal compute to jointly optimize a dense language and geometric representation of its surroundings. Lifelong LERF maintains this representation over time by detecting semantic changes and selectively updating these regions of the environment, avoiding the need to exhaustively remap. Human users can query inventory by providing natural language queries and receiving a 3D heatmap of potential object locations. To manage the computational load, we use Fog-ROS2, a cloud robotics platform, to offload resource-intensive tasks. Lifelong LERF obtains poses from a monocular RGBD SLAM backend, and uses these poses to progressively optimize a Language Embedded Radiance Field (LERF) for semantic monitoring. Experiments with 3-5 objects arranged on a tabletop and a Turtlebot with a RealSense camera suggest that Lifelong LERF can persistently adapt to changes in objects with up to 91% accuracy.