# Clutter Slices Approach for Identification-on-the-fly of Indoor Spaces

arXiv: https://arxiv.org/abs/2101.04262 · HTML: https://mlsanity.com/paper/2101.04262/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Indoor Space Recognition using Deep Convolutional Neural Network: A Case Study at MIT Campus](https://mlsanity.com/paper/1610.02414/) (2016)
- [Inspection-on-the-fly using Hybrid Physical Interaction Control for Aerial Manipulators](https://mlsanity.com/paper/2010.09605/) (2020)
- [Aerial Mobile Manipulator System to Enable Dexterous Manipulations with Increased Precision](https://mlsanity.com/paper/2010.09618/) (2020)

## Abstract (arXiv)

Construction spaces are constantly evolving, dynamic environments in need of continuous surveying, inspection, and assessment. Traditional manual inspection of such spaces proves to be an arduous and time-consuming activity. Automation using robotic agents can be an effective solution. Robots, with perception capabilities can autonomously classify and survey indoor construction spaces. In this paper, we present a novel identification-on-the-fly approach for coarse classification of indoor spaces using the unique signature of clutter. Using the context granted by clutter, we recognize common indoor spaces such as corridors, staircases, shared spaces, and restrooms. The proposed clutter slices pipeline achieves a maximum accuracy of 93.6% on the presented clutter slices dataset. This sensor independent approach can be generalized to various domains to equip intelligent autonomous agents in better perceiving their environment.