RGB-D SLAM in indoor dynamic environments with two channels based on scenario classification

Abstract

In visual simultaneous localization and mapping (SLAM) systems, the limitations of the assumption of scene rigidity are usually broken by using learning-based or geometry-based methods. However, learning-based methods usually have a high time cost, and geometry-based methods usually do not result in clean maps which are useful for advanced robotic applications. In this paper, an RGB-D SLAM in indoor dynamic environments with two channels that classifies frames as slightly and highly dynamic scenarios based on matching accuracy is proposed. And a geometric constraint based on Hamming distance is proposed to improve the effectiveness of matching accuracy as a basis for scenario classification. Dynamic features are detected by affine consistency constraint and semantic method. The semantic method is only used for highly dynamic scenarios to reduce the time cost of dynamic feature detection and provide a basis for mapping. Furthermore, an improved adaptive threshold algorithm is proposed to improve the robustness of feature matching. The proposed method is evaluated in the TUM RGB-D dataset and a real scenario. The experimental results demonstrate that the proposed method achieves highly accurate tracking with appreciable time cost in both slightly and highly indoor dynamic environments while obtaining effective maps.

SLM-SLAM: a visual SLAM system based on segmented large-scale model in dynamic scenes and zero-shot conditions

“…. , P n ∈ SE (3). The formulas for calculating ATE and RPE for the ith frame (1 ⩽ i ⩽ n) are as follows:…”

“…Leveraging self-positioning and constructed maps, it facilitates functionalities such as * Author to whom any correspondence should be addressed. autonomous navigation, 3D reconstruction, etc making it applicable in various domains, including autonomous driving, drones, service robots, and XR [1][2][3]. Due to the advantages of low cost and rich information provided by visual sensors, as well as the development of computer vision technology, visual SLAM (VSLAM) has gradually obtained a wide range of applications [4,5].…”

Degradation state detection and local map optimization for enhancing the SOTIF of map-matching-based fusion localization system

“…They employed histogram and particle filters for fusion with vehicle motion data to enhance the system's robustness during rainy conditions. In dynamic environments, Zhou et al [23] proposed a feature detection and matching algorithm for dynamic environments based on RGB-D SLAM, achieving high-precision tracking and mapping in dynamic environments. In tunnel scenarios, Kim et al [24] presented a high-precision vehicle localization method based on tunnel facilities, employing point feature maps and probability distribution maps.…”