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

TL;DR

DISCOMAN is a comprehensive indoor dataset with realistic sequences, designed to advance semantic SLAM by providing diverse data for training, benchmarking, and evaluating mapping and navigation algorithms.

Contribution

The paper introduces DISCOMAN, a large-scale indoor dataset with realistic rendering and ground truth, specifically aimed at benchmarking the mapping component of SLAM.

Findings

Benchmarking results for classical and learning-based SLAM algorithms.

Baseline mapping, semantic segmentation, and panoptic segmentation results.

Dataset enables evaluation of a wider range of SLAM methods.

Abstract

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.