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摘要:
为了使机器人准确使用全局地图、解决被绑架问题，设计了基于视觉内容匹配的自主定位系统，充分利用每个房间、每段走廊中的物体与布局信息进行机器人导航定位。视觉内容匹配包括提取图像重叠区、重叠区域的子块分解重建和相似度匹配。首先将待匹配图像中由机器人视觉和位移造成的畸变调整为一致、然后分析图像内容相似度，并设计了天花板特征点筛选和误匹配子块剔除算法，实现重叠区的准确提取与重建。实验中，机器人视觉可与关键帧序列构建的全局地图准确匹配，实时提取与之最相似的关键帧对机器人定位，匹配准确率不低于95%，定位精度RMSE < 0.5 m，被绑架时仍能准确自主定位。
Abstract:In order to take advantage of global navigation map for robot self-localization and solve kidnap problem, a robot vision localization system is presented based on graphic content matching. It can make good use of the different objects and their layout in different rooms or corridors to fix robot position, which cannot be disturbed by similar objects. This vision localization system is composed of frames overlap region extraction and overlap region rebuilding through sub-blocks matching, and the interference caused by points on the wall and mismatching sub-blocks can be deleted. The image distortion can be adjusted to the same before matching. In the experiment, this graphic matching method can match the real-time robot vision with keyframes global map effectively, and find out the most similar keyframe for each vision image and fix robot position exactly. More than 95% robot vision can be matched and position RMSE < 0.5 m. Robot can also localize itself effectively when it is kidnapped.
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Key words:
- robot vision /
- graphic content matching /
- image distortion /
- image overlap region /
- overlap region
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Abstract: Self-localization and mapping is an important and difficult problem for mobile robot. Reliable and low cost solution for this issue would promote the development of robotics industry. A robot vision localization system is presented in this paper, which can take advantage of global keyframes navigation map for robot self-localization. And two common problems for robot self-localization, including solve kidnap problem and similar objects interference, can be solved through this localization system, which could fix robot position by matching with global map according to the graphic content in the robot vision. The core of this system is graphic content matching, and composed by two parts: image overlap region extraction and overlap region rebuilding through sub-blocks matching. This method could match image content effectively. If two frames take some same objects, there would be some overlap regions between them. And the overlap regions between two frames can be obtained by translating and rotating these frames according to their matched feature points on the ceiling firstly. And a special designed ceiling feature point extraction and matching method is presented, and the interference caused by points on the wall and mismatching sub-blocks can be deleted according to the features of ceiling structure. After overlap region extraction, the graphic content matching can be processed in these regions. Through image matching, this localization system can make good use of the different objects and their layout in different rooms or corridors as landmarks. These landmarks can be used to fix robot global position precisely in the large indoor space, which is composed of multi-rooms and corridors. By taking advantage of image content, this vision system could make good use of the different objects in different rooms and cannot be disturbed by similar objects, which is common interference for global indoor environment localization. However, there would be some new interference for graphic content matching. The main interference is image distortion, which is caused by camera angle and robot movement. In order to revise image distortion and localize robot exactly, a graphic content matching method is presented. According to the features of image distortion, this matching method is designed through sub-blocks matching in the overlap regions between two frames. It could calculate the images similarity by adjusting the images to the same distortion. In the experiment, this graphic matching method can match the real-time robot vision with global keyframes map effectively, and find out the most similar keyframe for each vision image and fix robot position exactly. More than 95% robot vision can be matched and position RMSE < 0.5 m. Robot can also localize itself effectively when it is kidnapped.
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图 3 关键帧序列构建的全局地图(图中小方块为拍摄关键帧的位置).
Figure 3. The global map composed by keyframes (the blocks in the figure is keyframes taken position)
图 5 旋转和平移的结果. (a)关键帧平移. (b)机器人视觉旋转. (c)两帧重叠区域. (d)重叠区域的掩模.
Figure 5. The rotation and translation result. (a) Keyframe rotation. (b) Robot vision rotation. (c) Overlap region. (d) Overlap region mask.
图 6 天花板上的特征点所具备的连线与夹角特性. (a)机器视觉图像. (b)关键帧.
Figure 6. The features of connect lines and angle of features points. (a) Robot vision. (b) Keyframe.
图 7 图像特征点筛选结果(红点为特征点). (a)关键帧中的特征点. (b)视频帧中的特征点. (c)筛选后的关键帧中的特征点. (d)筛选后的视频帧中的特征点.
Figure 7. The screen result of feature points (red points). (a) The feature points in keyframe. (b) The feature points in robot vision. (c) The screen result of feature points in keyframe. (d) The screen result of feature points in robot vision.
图 8 重建过程. (a)机器人视觉的重叠区. (b)图(a)的子块分割. (c)关键帧的重叠区. (d)子块的匹配重建. (e)重建视觉图像重叠区.
Figure 8. The process of rebuilding. (a) The overlap region in robot vision. (b) The sub-block division of fig. (a). (c) The overlap region in keyframe. (d) The sub-block rebuilding. (e) The rebuilding result of overlap region of robot vision.
图 9 重叠区重建. (a)关键帧重叠区. (b)机器人视觉重叠区. (c)子块分解重建的视觉图像重叠区.
Figure 9. The rebuilding of overlap region. (a) The overlap region in keyframe. (b) The overlap region in robot vision. (c) The rebuilding result of overlap region of robot vision.
图 10 错误的重建重叠区. (a)关键帧. (b)机器人视觉. (c)视觉重叠区. (d)重叠区重建结果.
Figure 10. The wrong rebuilding overlap region. (a) Keyframe. (b) Robot vision. (b) The overlap region in robot vision. (c) The rebuilding result of overlap region of robot vision.
图 11 走廊里的实验结果. (a)关键帧. (b)机器人视觉. (c)重叠区. (d)重叠区重建结果.
Figure 11. The experiment result in the corridor. (a) Keyframe. (b) Robot vision. (c) The overlap region in robot vision. (d) The rebuilding result of overlap region of robot vision.
图 12 房间内的实验结果. (a)关键帧. (b)机器人视觉. (c)重叠区. (d)重叠区重建结果.
Figure 12. The experiment result in the room. (a) Keyframe. (b) Robot vision. (c) The overlap region in robot vision. (d) The rebuilding result of overlap region of robot vision.
图 13 机器人匹配定位和运动路径结算结果(圆点为机器人的自主定位结果).
Figure 13. The localization result and robot (The points are robot localization results).
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