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Graph Structure-Based Simultaneous Localization and Mapping Using a Hybrid Method of 2D Laser Scan and Monocular Camera Image in Environments with Laser Scan Ambiguity.

Oh T, Lee D, Kim H, Myung H - Sensors (Basel) (2015)

Bottom Line: To verify the effectiveness of the proposed method, real experiments were conducted in an indoor environment with a long corridor.The experimental results were compared with those of the conventional GMappingapproach.The results demonstrate that it is possible to localize the robot in environments with laser scan ambiguity in real time, and the performance of the proposed method is superior to that of the conventional approach.

View Article: PubMed Central - PubMed

Affiliation: Urban Robotics Laboratory (URL), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon 305-701, Korea. buljaga@kaist.ac.kr.

ABSTRACT
Localization is an essential issue for robot navigation, allowing the robot to perform tasks autonomously. However, in environments with laser scan ambiguity, such as long corridors, the conventional SLAM (simultaneous localization and mapping) algorithms exploiting a laser scanner may not estimate the robot pose robustly. To resolve this problem, we propose a novel localization approach based on a hybrid method incorporating a 2D laser scanner and a monocular camera in the framework of a graph structure-based SLAM. 3D coordinates of image feature points are acquired through the hybrid method, with the assumption that the wall is normal to the ground and vertically flat. However, this assumption can be relieved, because the subsequent feature matching process rejects the outliers on an inclined or non-flat wall. Through graph optimization with constraints generated by the hybrid method, the final robot pose is estimated. To verify the effectiveness of the proposed method, real experiments were conducted in an indoor environment with a long corridor. The experimental results were compared with those of the conventional GMappingapproach. The results demonstrate that it is possible to localize the robot in environments with laser scan ambiguity in real time, and the performance of the proposed method is superior to that of the conventional approach.

No MeSH data available.


The concept of the hybrid method. Coordinate systems of the monocular camera, the 2D laser scanner and the robot are shown. The depth value of the feature point can be acquired from the depth value of the laser scan point on the same uC.
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f2-sensors-15-15830: The concept of the hybrid method. Coordinate systems of the monocular camera, the 2D laser scanner and the robot are shown. The depth value of the feature point can be acquired from the depth value of the laser scan point on the same uC.

Mentions: In this subsection, the method for fusing the feature data of the camera and the depth information of the laser scanner is described. The 3D robot pose is predicted using a hybrid method, and this information serves as the constraints of the graph-based SLAM. The overall algorithm and concept are shown in Figures 1 and 2, respectively. Before performing the hybrid method of the laser scanner and the monocular camera, intrinsic calibration is necessary in order to determine the intrinsic parameter of the camera. It is also necessary to know the relative pose between the camera and the laser scanner for fusing data from them. Therefore, the relative pose information between the two sensors is obtained through extrinsic calibration.


Graph Structure-Based Simultaneous Localization and Mapping Using a Hybrid Method of 2D Laser Scan and Monocular Camera Image in Environments with Laser Scan Ambiguity.

Oh T, Lee D, Kim H, Myung H - Sensors (Basel) (2015)

The concept of the hybrid method. Coordinate systems of the monocular camera, the 2D laser scanner and the robot are shown. The depth value of the feature point can be acquired from the depth value of the laser scan point on the same uC.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541856&req=5

f2-sensors-15-15830: The concept of the hybrid method. Coordinate systems of the monocular camera, the 2D laser scanner and the robot are shown. The depth value of the feature point can be acquired from the depth value of the laser scan point on the same uC.
Mentions: In this subsection, the method for fusing the feature data of the camera and the depth information of the laser scanner is described. The 3D robot pose is predicted using a hybrid method, and this information serves as the constraints of the graph-based SLAM. The overall algorithm and concept are shown in Figures 1 and 2, respectively. Before performing the hybrid method of the laser scanner and the monocular camera, intrinsic calibration is necessary in order to determine the intrinsic parameter of the camera. It is also necessary to know the relative pose between the camera and the laser scanner for fusing data from them. Therefore, the relative pose information between the two sensors is obtained through extrinsic calibration.

Bottom Line: To verify the effectiveness of the proposed method, real experiments were conducted in an indoor environment with a long corridor.The experimental results were compared with those of the conventional GMappingapproach.The results demonstrate that it is possible to localize the robot in environments with laser scan ambiguity in real time, and the performance of the proposed method is superior to that of the conventional approach.

View Article: PubMed Central - PubMed

Affiliation: Urban Robotics Laboratory (URL), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon 305-701, Korea. buljaga@kaist.ac.kr.

ABSTRACT
Localization is an essential issue for robot navigation, allowing the robot to perform tasks autonomously. However, in environments with laser scan ambiguity, such as long corridors, the conventional SLAM (simultaneous localization and mapping) algorithms exploiting a laser scanner may not estimate the robot pose robustly. To resolve this problem, we propose a novel localization approach based on a hybrid method incorporating a 2D laser scanner and a monocular camera in the framework of a graph structure-based SLAM. 3D coordinates of image feature points are acquired through the hybrid method, with the assumption that the wall is normal to the ground and vertically flat. However, this assumption can be relieved, because the subsequent feature matching process rejects the outliers on an inclined or non-flat wall. Through graph optimization with constraints generated by the hybrid method, the final robot pose is estimated. To verify the effectiveness of the proposed method, real experiments were conducted in an indoor environment with a long corridor. The experimental results were compared with those of the conventional GMappingapproach. The results demonstrate that it is possible to localize the robot in environments with laser scan ambiguity in real time, and the performance of the proposed method is superior to that of the conventional approach.

No MeSH data available.