Evaluation of Modern Laser Based Indoor SLAM Algorithms

• Main Authors: Kirill Krinkin, Anton Filatov, Artyom Filatov, Artur Huletski, Dmitriy Kartashov
• Format: Article
• Language: English
• Published: FRUCT 2018-05-01
• Series: Proceedings of the XXth Conference of Open Innovations Association FRUCT
• Subjects: Indoor SLAM; laser SLAM; gmapping; cartographer; performance
• Online Access: https://fruct.org/publications/fruct22/files/Kri2.pdf

One of the key issues that prevents creation of a truly autonomous mobile robot is the simultaneous localization and mapping (SLAM) problem. A solution is supposed to estimate a robot pose and to build a map of an unknown environment simultaneously. Despite existence of different algorithms that try to solve the problem, the universal one has not been proposed yet [1]. A laser rangefinder is a widespread sensor for mobile platforms and it was decided to evaluate actual 2D laser scan based SLAM algorithms on real world indoor environments. The following algorithms were considered: Google Cartographer [2], GMapping [3], tinySLAM [4]. According to their evaluation, Cartographer and GMapping are more accurate than tinySLAM and Cartographer is the most robust of the algorithms.