DOARS: Dynamic Objects Aware RGB-D SLAM with Neural Rendering

Simultaneous Localization and Mapping (SLAM) refers to robots estimating their motion state and mapping surroundings using sensors in unknown environments. In dynamic scenes, occlusions and variation of disparities may lead to significant error in feature matching and erroneous pose estimation. For this reason, a novel method based on neural rendering is designed to address this problem. It employs semantic segmentation to extract masks of dynamic objects, using a purely static background to construct photometric and geometric loss functions. Additionally, joint feature-based pose estimation is integrated into the initialization during the tracking process, enhancing the network convergence speed and bolstering the resilience against motion blur. In the mapping phase, a keyframe addition strategy thar compares repaired images is proposed to refine the scene representation. This method has been validated on the TUM Dataset and compared with the existing traditional dynamic visual SLAM and neural rendering-based SLAM. The results reveal that it has doubled the mapping capability in highly dynamic scenes, demonstrating that it can significantly improve localization accuracy compared with other approaches.