# Place Categorization and Semantic Mapping on a Mobile Robot

arXiv: https://arxiv.org/abs/1507.02428 · HTML: https://mlsanity.com/paper/1507.02428/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2015.

## Built on by (most cited first, 12 of 12)

- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [A Survey on Open Set Recognition](https://mlsanity.com/paper/2109.00893/) (2021)
- [Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding](https://mlsanity.com/paper/2309.15065/) (2023)
- [MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D Sparse Convolutions](https://mlsanity.com/paper/2403.07593/) (2024)
- [QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding](https://mlsanity.com/paper/2404.06442/) (2024)
- [Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference](https://mlsanity.com/paper/2108.03180/) (2021)
- [Mapping High-level Semantic Regions in Indoor Environments without Object Recognition](https://mlsanity.com/paper/2403.07076/) (2024)
- [RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory](https://mlsanity.com/paper/2606.25206/) (2026)
- [Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information](https://mlsanity.com/paper/2103.06442/) (2021)
- [Topological Semantic Mapping by Consolidation of Deep Visual Features](https://mlsanity.com/paper/2106.12709/) (2021)
- [Evaluating Uncertainty Calibration for Open-Set Recognition](https://mlsanity.com/paper/2205.07160/) (2022)
- [An Image-based Approach of Task-driven Driving Scene Categorization](https://mlsanity.com/paper/2103.05920/) (2021)

## Abstract (arXiv)

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot's behaviour during navigation tasks. The system is made available to the community as a ROS module.