SFD-SLAM: a novel dynamic RGB-D SLAM based on saliency region detection

Abstract

Abstract In dynamic environments, several simultaneous localization and mapping (SLAM) systems effectively utilize optical flow fields to distinguish dynamic from static feature points. Commonly, these systems leverage the amplitude information within the optical flow field to develop adaptive thresholding segmentation models for identifying dynamic scene regions. Nevertheless, designing adaptive thresholding models typically necessitates meticulous planning and extensive experimentation. This study introduces a dynamic RGBD SLAM system, SFD-SLAM, which innovates by employing a saliency detection network for the direct extraction of dynamic regions via scene flow. This approach notably streamlines the design process associated with conventional adaptive thresholding models. Furthermore, SFD-SLAM incorporates a geometric module that merges depth residuals with hyperpixel segmentation to enhance the refinement of the dynamic mask. This is followed by integration with FCM clustering for the precise identification of moving objects. The efficacy of SFD-SLAM is assessed using the widely recognized TUM dynamic dataset. Experimental results demonstrate that the proposed system surpasses DGFlow-SLAM, which relies on an adaptive thresholding model for dynamic object segmentation, in terms of trajectory accuracy. It also achieves comparable localization accuracy to DynaSLAM. Moreover, SFD-SLAM maintains robust tracking capabilities, even in scenarios where DynaSLAM experiences tracking loss, thereby augmenting the robustness of RGBD-SLAM in dynamic settings.