# DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation

arXiv: https://arxiv.org/abs/1909.12146 · HTML: https://mlsanity.com/paper/1909.12146/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories](https://mlsanity.com/paper/2103.00262/) (2021)
- [Learning to Assess Danger from Movies for Cooperative Escape Planning in Hazardous Environments](https://mlsanity.com/paper/2207.13791/) (2022)

## Abstract (arXiv)

We present a novel dataset for training and benchmarking semantic SLAM methods. The dataset consists of 200 long sequences, each one containing 3000-5000 data frames. We generate the sequences using realistic home layouts. For that we sample trajectories that simulate motions of a simple home robot, and then render the frames along the trajectories. Each data frame contains a) RGB images generated using physically-based rendering, b) simulated depth measurements, c) simulated IMU readings and d) ground truth occupancy grid of a house. Our dataset serves a wider range of purposes compared to existing datasets and is the first large-scale benchmark focused on the mapping component of SLAM. The dataset is split into train/validation/test parts sampled from different sets of virtual houses. We present benchmarking results forboth classical geometry-based and recent learning-based SLAM algorithms, a baseline mapping method, semantic segmentation and panoptic segmentation.