Dyna-MSDepth: multi-scale self-supervised monocular depth estimation network for visual SLAM in dynamic scenes

Abstract

Monocular Simultaneous Localization And Mapping (SLAM) suffers from scale drift, leading to tracking failure due to scale ambiguity. Deep learning has significantly advanced self-supervised monocular depth estimation, enabling scale drift reduction. Nonetheless, current self-supervised learning approaches fail to provide scale-consistent depth maps, estimate depth in dynamic environments, or perceive multi-scale information. In response to these limitations, this paper proposes Dyna-MSDepth, a novel method for estimating multi-scale, stable, and reliable depth maps in dynamic environments. Dyna-MSDepth incorporates multi-scale high-order spatial semantic interaction into self-supervised training. This integration enhances the model’s capacity to discern intricate texture nuances and distant depth cues. Dyna-MSDepth is evaluated on challenging dynamic datasets, including KITTI, TUM, BONN, and DDAD, employing rigorous qualitative evaluations and quantitative experiments. Furthermore, the accuracy of the depth maps estimated by Dyna-MSDepth is assessed in monocular SLAM. Extensive experiments confirm the superior multi-scale depth estimation capabilities of Dyna-MSDepth, highlighting its significant value in dynamic environments. Code is available at https://github.com/Pepper-FlavoredChewingGum/Dyna-MSDepth.