# Surface Type Classification for Autonomous Robot Indoor Navigation

arXiv: https://arxiv.org/abs/1905.00252 · HTML: https://mlsanity.com/paper/1905.00252/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2019.

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

- [VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain](https://mlsanity.com/paper/2209.07725/) (2022)
- [Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping](https://mlsanity.com/paper/2204.11291/) (2022)
- [DogSurf: Quadruped Robot Capable of GRU-based Surface Recognition for Blind Person Navigation](https://mlsanity.com/paper/2402.03156/) (2024)
- [Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets](https://mlsanity.com/paper/2606.18698/) (2026)
- [Attention-Based Surface Representation Learning for Robot State Prediction and Open-Ended Surface Classification](https://mlsanity.com/paper/2610.04240/) (2026)

## Abstract (arXiv)

In this work we describe the preparation of a time series dataset of inertial measurements for determining the surface type under a wheeled robot. The data consists of over 7600 labeled time series samples, with the corresponding surface type annotation. This data was used in two public competitions with over 1500 participant in total. Additionally, we describe the performance of state-of-art deep learning models for time series classification, as well as propose a baseline model based on an ensemble of machine learning methods. The baseline achieves an accuracy of over 68% with our nine-category dataset.