# Online Object-Oriented Semantic Mapping and Map Updating

arXiv: https://arxiv.org/abs/2011.06895 · HTML: https://mlsanity.com/paper/2011.06895/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation](https://mlsanity.com/paper/2210.09739/) (2022)
- [3D Semantic Scene Perception using Distributed Smart Edge Sensors](https://mlsanity.com/paper/2205.01460/) (2022)
- [Object-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots](https://mlsanity.com/paper/2407.06077/) (2024)
- [Planning for Learning Object Properties](https://mlsanity.com/paper/2301.06054/) (2023)
- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles](https://mlsanity.com/paper/2108.06608/) (2021)
- [Build Once, Monitor Continuously: Persistent Semantic Mapping via Autonomous Exploration and Open-Vocabulary Object Updates](https://mlsanity.com/paper/2409.15493/) (2024)

## Abstract (arXiv)

Creating and maintaining an accurate representation of the environment is an essential capability for every service robot. Especially for household robots acting in indoor environments, semantic information is important. In this paper, we present a semantic mapping framework with modular map representations. Our system is capable of online mapping and object updating given object detections from RGB-D data and provides various 2D and 3D~representations of the mapped objects. To undo wrong data associations, we perform a refinement step when updating object shapes. Furthermore, we maintain an existence likelihood for each object to deal with false positive and false negative detections and keep the map updated. Our mapping system is highly efficient and achieves a run time of more than 10 Hz. We evaluated our approach in various environments using two different robots, i.e., a Toyota HSR and a Fraunhofer Care-O-Bot-4. As the experimental results demonstrate, our system is able to generate maps that are close to the ground truth and outperforms an existing approach in terms of intersection over union, different distance metrics, and the number of correct object mappings