Robust SLAM Systems: Are We There Yet?

Horia-Mihai Bujanca, Xuesong Shi, Matthew Spear, Pengpeng Zhao, Barry Lennox, Mikel Luján

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Progress in the last decade has brought about significant improvements in the accuracy and speed of SLAM systems, broadening their mapping capabilities. Despite these advancements, long-term operation remains a major challenge, primarily due to the wide spectrum of perturbations robotic systems may encounter.

Increasing the robustness of SLAM algorithms is an ongoing effort, however it usually addresses a specific perturbation. Generalisation of robustness across a large variety of challenging scenarios is not well-studied nor understood. This paper presents a systematic evaluation of the robustness of open-source state-of-the-art SLAM algorithms with respect to challenging conditions such as fast motion, non-uniform illumination, and dynamic scenes. The experiments are performed with perturbations present both independently of each other, as well as in combination in long-term deployment settings in unconstrained environments (lifelong operation).

The detailed results (approx. 20,000 experiments) along with comprehensive documentation of the benchmarking tool for integrating new datasets and evaluating SLAM algorithms not studied in this work are available at
Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)
Number of pages8
Publication statusAccepted/In press - Sep 2021


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