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摘要: 人类的视觉注意具有高度的选择性.模仿这些机制可以使得机器人对其周围环境建模更具高效、智能和鲁棒特性.本文采用视觉注意提出了一种移动机器人环境3D建模方法.该方法采用障碍物距离势函数的变化率作为显著度的度量函数，利用移动机器人提取到的场景中的特征点并结合快速均值漂移算法，实现了移动机器人周围环境中物体显著性检测，并以其为栅格先验模型，结合传感器模型、投影方法采用贝叶斯估计方法构建了环境的栅格模型.建立的模型在室内和室外环境进行了实验验证和性能评估.Abstract: Human visual attention is highly selective. The artificial vision system that imitates this mechanism increases the efficiency, intelligence, and robustness of mobile robots in environment modeling. This paper presents a 3-D modeling method based on visual attention for mobile robots. This method uses the distance-potential gradient as motion contrast and combines the visual features extracted from the scene with a mean shift segment algorithm to detect conspicuous objects in the surrounding environment. This method takes the saliency of objects as priori information, uses Bayes' theorem to fuse sensor modeling and grid priori modeling, and uses the projection method to create and update the 3-D environment modeling. The results of the experiments and performance evaluation illustrate the capabilities of our approach in generating accurate 3-D maps.
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Key words:
- 3-D Mapping /
- grid model /
- mobile robots /
- visual attention
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Fig. 1 The robot system used in our evaluations. (a) Mobile robot platform. (b) Binocular visual system and DM642 image processing card.
Fig. 2 Schematic overview of the method. The dash box is used for saliency modeling construction, and a 3-D map is generated by using the probability of occupancy.
Fig. 3 The relation of $\Delta \varphi$ , $p$ ( $x$ , $y$ , $z$ ), and $p_i$ ( $x_i$ , $y_i$ , $z_i$ ), where $p$ ( $x$ , $y$ , $z$ ) is the center of the robot and $p_i$ ( $x_i$ , $y_i$ , $z_i$ ) is the feature point on the objects.
Fig. 4 Occupancy grid maps creation when objects produce visual occlusion. We use the projection method to calculate the objects occupancy width w.
Fig. 5 Overhead view of occupancy grid updating. The updated results using (12) will be close to the size of objects when robot moves from $A$ to $B$ . The shadows are the occupancy fields of occlusion.
Fig. 6 An example of a saliency map. (a) Conspicuous objects. Parts 1 and 3 are of the same object; however, because of the differences in texture and distance, the object was segmented into two parts. (b) Indoor corridor and conspicuous SURF features.
Fig. 7 A part of 3-D occupancy grid map. (a) The map is created using SURF features, and many grids show discontinuity. (b) The map is created by combining the conspicuous SURF features with the mean shift algorithm, and the discontinuity is reduced significantly.
Fig. 8 Indoor corridor 3-D map with 1 dm $^3$ voxel size. The dash line and arrow are the trajectory and moving direction of mobile robot, respectively, and the small circles are the places used to evaluate the performance.
Fig. 9 Outdoor 3-D map with 1 dm3 voxel size, where the dashed line and arrow are the trajectory and moving direction of mobile robot, respectively, and the little circles are the places used to evaluate the performance.
Fig. 12 Evaluation of mapping accuracy under (a) stable illumination and (b) varying illumination. The results are obtained with SURF and the mapping error increases under varying illumination, however, VSM slightly increases.
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