A Real World Visual SLAM Dataset for Indoor Construction Sites

12 pages•Published: August 28, 2025

Abstract

This paper presents a RGBD slam construction dataset with a mounted platform, designed to collect the unique challenges encountered in construction sites. An Ouster OS0-128 LiDAR is utilized as the sensor of LiDAR SLAM, working as the ground truth for localization. Our dataset records various construction settings with different stages of building materials and structures, such as concrete, brick, plaster, and putty, providing a comprehensive benchmark for training and evaluating SLAM algorithms. Through testing on current SLAM algorithms, we demonstrate the limitations of traditional approaches in these environments and provide a VINS based algorithm as the benchmark. This dataset serves as a valuable resource for researchers aiming to enhance SLAM performance in the real construction environments. The detailed information of the dataset is available at https://github.com/WenyuLWY/HCIC-Construction-VSLAM-Dataset.git

Keyphrases: construction robot, dataset, visual slam

In: Jack Cheng and Yu Yantao (editors). Proceedings of The Sixth International Conference on Civil and Building Engineering Informatics, vol 22, pages 368-379.

Links:	https://easychair.org/publications/paper/wQ3d
	https://doi.org/10.29007/csqt

BibTeX entry

@inproceedings{ICCBEI2025:Real_World_Visual_SLAM,
  author    = {Wenyu Li and Xinyu Chen and Yantao Yu},
  title     = {A Real World Visual SLAM Dataset for Indoor Construction Sites},
  booktitle = {Proceedings of The Sixth International Conference on Civil and Building Engineering Informatics},
  editor    = {Jack Cheng and Yu Yantao},
  series    = {Kalpa Publications in Computing},
  volume    = {22},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/wQ3d},
  doi       = {10.29007/csqt},
  pages     = {368-379},
  year      = {2025}}

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