Method and apparatus for simultaneous localization and mapping of robot
A SLAM of a robot is provided. The position of a robot and the position of feature data may be estimated by acquiring an image of the robot's surroundings, extracting feature data from the image, and matching the extracted feature data with registered feature data. Furthermore, measurement update is performed in a camera coordinate system and an appropriate assumption is added upon coordinate conversion, thereby reducing non-linear components and thus improving the SLAM performance.
1. A simultaneous localization and mapping (SLAM) apparatus of a robot, comprising:
a converter to convert a two-dimensional (2D) position of measured feature data in a first coordinate system to a three-dimensional (3D) position in a second coordinate system based on a preset depth of the measured feature data, the depth of the measured feature data having been preset to a predetermined value; and
an updater to update pre-existing registered feature data in a third coordinate system and corresponding to the measured feature data, by converting the registered feature data from the third coordinate system to the second coordinate system and comparing, in the second coordinate system, the converted 3D position of the measured feature data with a corresponding 3D position of the converted registered feature data.
2. The SLAM apparatus of claim 1 , wherein the converter converts the 2D position of the measured feature data to a 3D position in a camera coordinate system of a camera for acquiring the measured feature data.
3. A simultaneous localization and mapping (SLAM) apparatus of a robot, comprising:
a converter to convert a two-dimensional (2D) position of measured feature data in an image coordinate system to a three-dimensional (3D) position in a camera coordinate system based on a preset depth of the measured feature data, the depth of the measured feature data having been preset to a predetermined value; and
an updater to update registered feature data in a world coordinate system by converting the registered feature data from the world coordinate system to the camera coordinate system and comparing, in the camera coordinate system, the converted 3D position of the measured feature data with a 3D position of the converted registered feature data,
wherein the updater updates the registered feature data in the camera coordinate system of a camera for acquiring the measured feature data.
4. The SLAM apparatus of claim 1 , wherein the converting is based on the measured feature data being a single feature point.
5. A simultaneous localization and mapping (SLAM) apparatus of a robot, comprising:
a converter to convert a two-dimensional (2D) position of measured feature data in a first coordinate system to a three-dimensional (3D) position in a second coordinate system; and
an updater to update pre-existing registered feature data in a third coordinate system and corresponding to the measured feature data, by converting the registered feature data from the third coordinate system to the second coordinate system and comparing, in the second coordinate system, the converted 3D position of the measured feature data with a corresponding 3D position of the converted registered feature data,
wherein the feature data is a feature point, and
wherein, in the converting of the 2D position to the 3D position, the converter sets a depth of the feature point to a predetermined value and sets an error covariance in the depth direction to infinity.
6. The SLAM apparatus of claim 1 , wherein the converting is based on the measured feature data being a single feature line, and a position of the feature line is defined to be a 2D position of both defined end points of the feature line.
7. The SLAM apparatus of claim 6 , wherein, in the converting of the 2D position to the 3D position, the converter presets the depth of the measured feature data to the predetermined value by presetting a depth of each end point to respective predetermined values and the converter sets an error covariance in the depth direction and an error covariance in a direction parallel to the feature line to infinity, such that the converting is then based on the set depths and error covariances.
8. A simultaneous localization and mapping (SLAM) method of a robot, comprising:
converting a two-dimensional (2D) position of measured feature data in a first coordinate system to a three-dimensional (3D) position in a second coordinate system based on a preset depth of the measured feature data, the depth of the measured feature data having been preset to a predetermined value; and
updating, using at least one processing device, pre-existing registered feature data in a third coordinate system and corresponding to the measured feature data, by converting the registered feature data from the third coordinate system to the second coordinate system and comparing, in the second coordinate system, the converted 3D position of the measured feature data with a corresponding 3D position of the converted registered feature data.
9. The SLAM method of claim 8 , wherein the 2D position of the measured feature data is converted to a 3D position in a camera coordinate system of a camera for acquiring the measured feature data.
10. The SLAM method of claim 8 , wherein updating is performed in a camera coordinate system of a camera for acquiring the measured feature data.
11. The SLAM method of claim 8 , wherein the converting is based on the measured feature data being a single feature point.
12. The SLAM method of claim 11 , wherein the converting further comprises setting an error covariance in the depth direction to infinity when converting the 2D position to the 3D position, such that the converting is then based on the set depth and error covariance.
13. The SLAM method of claim 8 , wherein the converting is based on the measured feature data being a single feature line, and a position of the feature line is defined to be a 2D position of both defined end points of the feature line.
14. The SLAM method of claim 13 , wherein the converting further comprises:
presetting the depth of the measured feature data to the predetermined value by presetting a depth of each end point to respective predetermined values; and
setting an error covariance in the depth direction and an error covariance in a direction parallel to the feature line to infinity when converting the 2D position to the 3D position,
wherein the converting is then based on the set depths and error covariances.
15. The SLAM apparatus of claim 4 , wherein, in the converting of the 2D position to the 3D position, the converter sets an error covariance in the depth direction to infinity, such that the converting is then based on the set depth and error covariance.