TY - GEN

T1 - Understanding Why SLAM Algorithms Fail in Modern Indoor Environments

AU - Nwankwo, Linus

AU - Rueckert, Elmar

N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023/5/27

Y1 - 2023/5/27

N2 - 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.

AB - 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.

KW - Mapping

KW - SLAM algorithms

KW - SLAM evaluation

UR - http://www.scopus.com/inward/record.url?scp=85163415309&partnerID=8YFLogxK

UR - https://cloud.cps.unileoben.ac.at/index.php/s/KdZ2E2np5QEnYfL

U2 - 10.1007/978-3-031-32606-6_22

DO - 10.1007/978-3-031-32606-6_22

M3 - Conference contribution

AN - SCOPUS:85163415309

T3 - International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)

SP - 186

EP - 194

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

T2 - 32nd International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2023

Y2 - 14 June 2023 through 16 June 2023

ER -