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Runzhi Wang
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20A, Datun Road, Chaoyang District, Beijing 100101, China

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Journal article
Published: 01 November 2019 in Remote Sensing
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High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as them being labor-intensive, of poor accuracy, and time-consuming, this paper proposes a novel indoor-positioning method with automated red, green, blue and depth (RGB-D) image database construction. First, strategies for automated database construction are developed to reduce the workload of manually selecting database images and ensure the requirements of high-accuracy indoor positioning. The database is automatically constructed according to the rules, which is more objective and improves the efficiency of the image-retrieval process. Second, by combining the automated database construction module, convolutional neural network (CNN)-based image-retrieval module, and strict geometric relations-based pose estimation module, we obtain a high-accuracy indoor-positioning system. Furthermore, in order to verify the proposed method, we conducted extensive experiments on the public indoor environment dataset. The detailed experimental results demonstrated the effectiveness and efficiency of our indoor-positioning method.

ACS Style

Runzhi Wang; Wenhui Wan; Kaichang Di; Ruilin Chen; Xiaoxue Feng. A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction. Remote Sensing 2019, 11, 2572 .

AMA Style

Runzhi Wang, Wenhui Wan, Kaichang Di, Ruilin Chen, Xiaoxue Feng. A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction. Remote Sensing. 2019; 11 (21):2572.

Chicago/Turabian Style

Runzhi Wang; Wenhui Wan; Kaichang Di; Ruilin Chen; Xiaoxue Feng. 2019. "A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction." Remote Sensing 11, no. 21: 2572.

Journal article
Published: 14 May 2019 in Remote Sensing
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Simultaneous localization and mapping (SLAM) methods based on an RGB-D camera have been studied and used in robot navigation and perception. So far, most such SLAM methods have been applied to a static environment. However, these methods are incapable of avoiding the drift errors caused by moving objects such as pedestrians, which limits their practical performance in real-world applications. In this paper, a new RGB-D SLAM with moving object detection for dynamic indoor scenes is proposed. The proposed detection method for moving objects is based on mathematical models and geometric constraints, and it can be incorporated into the SLAM process as a data filtering process. In order to verify the proposed method, we conducted sufficient experiments on the public TUM RGB-D dataset and a sequence image dataset from our Kinect V1 camera; both were acquired in common dynamic indoor scenes. The detailed experimental results of our improved RGB-D SLAM were summarized and demonstrate its effectiveness in dynamic indoor scenes.

ACS Style

Runzhi Wang; Wenhui Wan; Yongkang Wang; Kaichang Di. A New RGB-D SLAM Method with Moving Object Detection for Dynamic Indoor Scenes. Remote Sensing 2019, 11, 1143 .

AMA Style

Runzhi Wang, Wenhui Wan, Yongkang Wang, Kaichang Di. A New RGB-D SLAM Method with Moving Object Detection for Dynamic Indoor Scenes. Remote Sensing. 2019; 11 (10):1143.

Chicago/Turabian Style

Runzhi Wang; Wenhui Wan; Yongkang Wang; Kaichang Di. 2019. "A New RGB-D SLAM Method with Moving Object Detection for Dynamic Indoor Scenes." Remote Sensing 11, no. 10: 1143.

Journal article
Published: 20 October 2018 in Sensors
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In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary features—point and line segments—have been widely used to calculate the pose of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.

ACS Style

Runzhi Wang; Kaichang Di; Wenhui Wan; Yongkang Wang. Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes. Sensors 2018, 18, 3559 .

AMA Style

Runzhi Wang, Kaichang Di, Wenhui Wan, Yongkang Wang. Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes. Sensors. 2018; 18 (10):3559.

Chicago/Turabian Style

Runzhi Wang; Kaichang Di; Wenhui Wan; Yongkang Wang. 2018. "Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes." Sensors 18, no. 10: 3559.