Real-Time Global Registration for Globally Consistent RGB-D SLAM

Han, Lei; Xu, Lan; Bobkov, Dmytro; Steinbach, Eckehard; Fang, Lü

doi:10.1109/tro.2018.2882730

Cited by 44 publications

(25 citation statements)

References 25 publications

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“…To reduce cumulative errors, a vertex-to-edge weighted closed-form algorithm [34] is used to reduce the camera drift error for dense RGB-D SLAM, which results in significantly low trajectory error than several state-of-the-art methods; in addition, this research has received great attention for the improving real-time performance. The back-end optimization problem is decoupled into linear components (feature position) and nonlinear components (camera poses) [35], and as a result complexity is significantly reduced without compromising accuracy; in addition, this algorithm can achieve globally consistent pose estimation in real-time via CPU computing. Thus, it is clear that, after various improvements, all of the above-mentioned RGB-D SLAM algorithms generally achieve better results in indoor environments involving small scenes.…”

Section: Related Workmentioning

confidence: 99%

Real-Time RGB-D Simultaneous Localization and Mapping Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation

Kang

Fan

et al. 2019

Applied Sciences

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In recent years, low-cost and lightweight RGB and depth (RGB-D) sensors, such as Microsoft Kinect, have made available rich image and depth data, making them very popular in the field of simultaneous localization and mapping (SLAM), which has been increasingly used in robotics, self-driving vehicles, and augmented reality. The RGB-D SLAM constructs 3D environmental models of natural landscapes while simultaneously estimating camera poses. However, in highly variable illumination and motion blur environments, long-distance tracking can result in large cumulative errors and scale shifts. To address this problem in actual applications, in this study, we propose a novel multithreaded RGB-D SLAM framework that incorporates a highly accurate prior terrestrial Light Detection and Ranging (LiDAR) point cloud, which can mitigate cumulative errors and improve the system’s robustness in large-scale and challenging scenarios. First, we employed deep learning to achieve system automatic initialization and motion recovery when tracking is lost. Next, we used terrestrial LiDAR point cloud to obtain prior data of the landscape, and then we applied the point-to-surface inductively coupled plasma (ICP) iterative algorithm to realize accurate camera pose control from the previously obtained LiDAR point cloud data, and finally expanded its control range in the local map construction. Furthermore, an innovative double window segment-based map optimization method is proposed to ensure consistency, better real-time performance, and high accuracy of map construction. The proposed method was tested for long-distance tracking and closed-loop in two different large indoor scenarios. The experimental results indicated that the standard deviation of the 3D map construction is 10 cm in a mapping distance of 100 m, compared with the LiDAR ground truth. Further, the relative cumulative error of the camera in closed-loop experiments is 0.09%, which is twice less than that of the typical SLAM algorithm (3.4%). Therefore, the proposed method was demonstrated to be more robust than the ORB-SLAM2 algorithm in complex indoor environments.

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Section: Related Workmentioning

confidence: 99%

Real-Time RGB-D Simultaneous Localization and Mapping Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation

Kang

Fan

et al. 2019

Applied Sciences

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“…After recent years of development, RGB-D SLAM technology tends to be mature [38,39] and has achieved good experimental results. However, the RGB-D SLAM algorithm still has some shortcomings.…”

Section: Related Workmentioning

confidence: 99%

A Novel RGB-D SLAM Algorithm Based on Cloud Robotics

Li¹,

Zhang²,

Huang³

2019

Sensors

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In this paper, we present a novel red-green-blue-depth simultaneous localization and mapping (RGB-D SLAM) algorithm based on cloud robotics, which combines RGB-D SLAM with the cloud robot and offloads the back-end process of the RGB-D SLAM algorithm to the cloud. This paper analyzes the front and back parts of the original RGB-D SLAM algorithm and improves the algorithm from three aspects: feature extraction, point cloud registration, and pose optimization. Experiments show the superiority of the improved algorithm. In addition, taking advantage of the cloud robotics, the RGB-D SLAM algorithm is combined with the cloud robot and the back-end part of the computationally intensive algorithm is offloaded to the cloud. Experimental validation is provided, which compares the cloud robotic-based RGB-D SLAM algorithm with the local RGB-D SLAM algorithm. The results of the experiments demonstrate the superiority of our framework. The combination of cloud robotics and RGB-D SLAM can not only improve the efficiency of SLAM but also reduce the robot’s price and size.

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“…The key is to detect the closed loop correctly [8], [9]. Compared with the single spatial structure perception information of the laser sensor, the visual sensor has huge advantages and potential in improving the accuracy of inter-frame estimation and the accuracy rate of closed-loop detection with rich perceptual information such as color and texture [10]- [12]. Visual SLAM (SLAM) is a SLAM system with image as the main source of environmental perception information.…”

Section: Introductionmentioning

confidence: 99%

3D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment

Cui

Wang

2020

IEEE Access

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Although the existing localization and mapping (SLAM) technology of indoor mobile robot has made great development, its intelligence and environmental perception ability still cannot meet the needs of service and inspection. Therefore, based on edge computing environment, a 3D semantic map construction of mobile robot based on improved ORB-SALM2 is proposed. Firstly, the improved yolov3 algorithm is used to detect indoor objects, and then the real-time semantic segmentation network model based on deep learning is used to segment indoor objects to achieve the classification of pixel points of objects on two-dimensional images, and BAFF feature fusion algorithm is introduced to improve the accuracy of semantic segmentation model. Then, through the SLAM system, we estimate the pose of the image in the result of semantic segmentation, and use the depth information to project it into the three-dimensional environment to build the three-dimensional semantic map. Finally, the experiment platform of mobile robot is built to verify the stability of ORB-D and thermal imaging sensor registration technology, the accuracy and real-time of building three-dimensional environment thermal field map, and the accuracy of robot positioning using thermal infrared and depth image.

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Real-Time Global Registration for Globally Consistent RGB-D SLAM

Cited by 44 publications

References 25 publications

Real-Time RGB-D Simultaneous Localization and Mapping Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation

Real-Time RGB-D Simultaneous Localization and Mapping Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation

A Novel RGB-D SLAM Algorithm Based on Cloud Robotics

3D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment

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