Dropping laser scans for SLAM when they contribute with no relevant information

Kirill Krinkin, Anton Filatov, Correlation filter of 2D laser scans for indoor environment, . Robotics and Autonomous Systems, Volume 142, 2021 DOI: 10.1016/j.robot.2021.103809.

Modern laser SLAM (simultaneous localization and mapping) and structure from motion algorithms face the problem of processing redundant data. Even if a sensor does not move, it still continues to capture scans that should be processed. This paper presents the novel filter that allows dropping 2D scans that bring no new information to the system. Experiments on MIT and TUM datasets show that it is possible to drop more than half of the scans. Moreover the paper describes the formulas that enable filter adaptation to a particular robot with known speed and characteristics of lidar. In addition, the indoor corridor detector is introduced that also can be applied to any specific shape of a corridor and sensor.

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