# Topological Semantic Mapping by Consolidation of Deep Visual Features

arXiv: https://arxiv.org/abs/2106.12709 · HTML: https://mlsanity.com/paper/2106.12709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 24 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks](https://mlsanity.com/paper/1609.05130/) (2016)
- [Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery](https://mlsanity.com/paper/1903.00268/) (2019)
- [Meaningful Maps With Object-Oriented Semantic Mapping](https://mlsanity.com/paper/1609.07849/) (2016)
- [Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras](https://mlsanity.com/paper/1703.08866/) (2017)
- [DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks](https://mlsanity.com/paper/1703.03098/) (2017)
- [Place Categorization and Semantic Mapping on a Mobile Robot](https://mlsanity.com/paper/1507.02428/) (2015)
- [Learning Deep NBNN Representations for Robust Place Categorization](https://mlsanity.com/paper/1702.07898/) (2017)
- [Incremental Semantic Mapping with Unsupervised On-line Learning](https://mlsanity.com/paper/1907.04001/) (2019)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping](https://mlsanity.com/paper/2203.10820/) (2022)

## Abstract (arXiv)

Many works in the recent literature introduce semantic mapping methods that use CNNs (Convolutional Neural Networks) to recognize semantic properties in images. The types of properties (eg.: room size, place category, and objects) and their classes (eg.: kitchen and bathroom, for place category) are usually predefined and restricted to a specific task. Thus, all the visual data acquired and processed during the construction of the maps are lost and only the recognized semantic properties remain on the maps. In contrast, this work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN (GoogLeNet), from 2D images captured in multiple views of the environment as the robot operates, to create, through averages, consolidated representations of the visual features acquired in the regions covered by each topological node. These representations allow flexible recognition of semantic properties of the regions and use in other visual tasks. Experiments with a real-world indoor dataset showed that the method is able to consolidate the visual features of regions and use them to recognize objects and place categories as semantic properties, and to indicate the topological location of images, with very promising results.