Correlation Filter of 2D Laser Scans For Indoor Environment
This addresses computational efficiency for indoor robotic navigation systems, though it appears incremental as it builds on existing SLAM methods.
The paper tackles the problem of redundant data processing in laser SLAM by introducing a novel filter that drops 2D scans with no new information, achieving a reduction of over half the scans on MIT and TUM datasets.
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 thepaper 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.