Loop closure detection is crucial in a SLAM system to ensure a globally consistent map by reducing drift accumulation. Similar to other fields, handcrafted methods are starting to be replaced by learning-based approaches. Panoptic segmentation, which ...
Loop closure detection is crucial in a SLAM system to ensure a globally consistent map by reducing drift accumulation. Similar to other fields, handcrafted methods are starting to be replaced by learning-based approaches. Panoptic segmentation, which fuses instance and semantic segmentation, enables a richer understanding of the surrounding environment. The proposed method is based on the Scan Context++, further improved by integrating panoptic information into the generated descriptors. A panoptic similarity calculation, based on histogram and Earth Mover’s Distance, is performed and combined with the geometric distance similarity calculation, based on Scan Context++. Experimental results show improvements compared to the baseline approach and other methods, with a lower threshold suggesting higher sensitivity.