Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes
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 ca...
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| Format: | Article |
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MDPI AG
2018-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/10/3559 |
| _version_ | 1798039402599940096 |
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| author | Runzhi Wang Kaichang Di Wenhui Wan Yongkang Wang |
| author_facet | Runzhi Wang Kaichang Di Wenhui Wan Yongkang Wang |
| author_sort | Runzhi Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:53:24Z |
| format | Article |
| id | doaj.art-bab14a5289cb495bb6a0e238c0f2ae77 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:53:24Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bab14a5289cb495bb6a0e238c0f2ae772022-12-22T04:01:11ZengMDPI AGSensors1424-82202018-10-011810355910.3390/s18103559s18103559Improved Point-Line Feature Based Visual SLAM Method for Indoor ScenesRunzhi Wang0Kaichang Di1Wenhui Wan2Yongkang Wang3State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20A, Datun Road, Chaoyang District, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20A, Datun Road, Chaoyang District, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20A, Datun Road, Chaoyang District, Beijing 100101, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaIn 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.http://www.mdpi.com/1424-8220/18/10/3559indoor visual SLAMadaptive modelmotion estimationstereo camera |
| spellingShingle | Runzhi Wang Kaichang Di Wenhui Wan Yongkang Wang Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes Sensors indoor visual SLAM adaptive model motion estimation stereo camera |
| title | Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes |
| title_full | Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes |
| title_fullStr | Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes |
| title_full_unstemmed | Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes |
| title_short | Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes |
| title_sort | improved point line feature based visual slam method for indoor scenes |
| topic | indoor visual SLAM adaptive model motion estimation stereo camera |
| url | http://www.mdpi.com/1424-8220/18/10/3559 |
| work_keys_str_mv | AT runzhiwang improvedpointlinefeaturebasedvisualslammethodforindoorscenes AT kaichangdi improvedpointlinefeaturebasedvisualslammethodforindoorscenes AT wenhuiwan improvedpointlinefeaturebasedvisualslammethodforindoorscenes AT yongkangwang improvedpointlinefeaturebasedvisualslammethodforindoorscenes |