DLD-SLAM: RGB-D Visual Simultaneous Localisation and Mapping in Indoor Dynamic Environments Based on Deep Learning

Abstract

This work presents a novel RGB-D dynamic Simultaneous Localisation and Mapping (SLAM) method that improves the precision, stability, and efficiency of localisation while relying on lightweight deep learning in a dynamic environment compared to the traditional static feature-based visual SLAM algorithm. Based on ORB-SLAM3, the GCNv2-tiny network instead of the ORB method, improves the reliability of feature extraction and matching and the accuracy of position estimation; then, the semantic segmentation thread employs the lightweight YOLOv5s object detection algorithm based on the GSConv network combined with a depth image to determine potentially dynamic regions of the image. Finally, to guarantee that the static feature points are used for position estimation, dynamic probability is employed to determine the true dynamic feature points based on the optical flow, semantic labels, and the state in last frame. We have performed experiments on the TUM datasets to verify the feasibility of the algorithm. Compared with the classical dynamic visual SLAM algorithm, the experimental results demonstrate that the absolute trajectory error is greatly reduced in dynamic environments, and that the computing efficiency is improved by 31.54% compared with the real-time dynamic visual SLAM algorithm with close accuracy, demonstrating the superiority of DLD-SLAM in accuracy, stability, and efficiency.

BinVPR: Binary Neural Networks towards Real-Valued for Visual Place Recognition

“…Pre-trained CNN models are usually used as feature extractors. Sünderhauf et al [14] and Nasser et al [15] used AlexNet pre-trained on the ImageNet dataset. PlaceNet [16] is based on the same principle and is trained on a large dataset called Places365, which is organized into 365 categories.…”

“…VSLAM [15] (Visual Simultaneous Localization and Mapping) uses visual information to tasks if it is possible for a mobile robot to be placed at an unknown location in an unknown environment, at the same time, and builds a consistent map of this environment while simultaneously determining its location within this map. VSLAM systems have four main components: visual odometry, Loop Closure Detection (LCD), back-end optimization, and mapping.…”