Understanding Why SLAM Algorithms Fail in Modern Indoor Environments

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Simultaneous localization and mapping (SLAM) algorithms are essential for the autonomous navigation of mobile robots. With the increasing demand for autonomous systems, it is crucial to evaluate and compare the performance of these algorithms in real-world environments.
In this paper, we provide an evaluation strategy and real-world datasets to test and evaluate SLAM algorithms in complex and challenging indoor environments. Further, we analysed state-of-the-art (SOTA) SLAM algorithms based on various metrics such as absolute trajectory error, scale drift, and map accuracy and consistency. Our results demonstrate that SOTA SLAM algorithms often fail in challenging environments, with dynamic objects, transparent and reflecting surfaces. We also found that successful loop closures had a significant impact on the algorithm’s performance. These findings highlight the need for further research to improve the robustness of the algorithms in real-world scenarios.
Original languageEnglish
Title of host publicationInternational Conference on Robotics in Alpe-Adria-Danube Region (RAAD)
Number of pages9
Publication statusPublished - 27 May 2023
Event32nd International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2023 - Bled, Slovenia
Duration: 14 Jun 202316 Jun 2023

Publication series

NameInternational Conference on Robotics in Alpe-Adria-Danube Region (RAAD)


Conference32nd International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Mapping
  • SLAM algorithms
  • SLAM evaluation

Cite this